A classification algorithm that derives weighted sum scores for insight into disease
- Quinn, Anthony, Stranieri, Andrew, Yearwood, John, Hafen, Gaudenz
- Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John , Hafen, Gaudenz
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Third Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2009), Wellington, New Zealand : Vol. 97, p. 13-17
- Full Text:
- Description: Data mining is often performed with datasets associated with diseases in order to increase insights that can ultimately lead to improved prevention or treatment. Classification algorithms can achieve high levels of predictive accuracy but have limited application for facilitating the insight that leads to deeper understanding of aspects of the disease. This is because the representation of knowledge that arises from classification algorithms is too opaque, too complex or too sparse to facilitate insight. Clustering, association and visualisation approaches enable greater scope for clinicians to be engaged in a way that leads to insight, however predictive accuracy is compromised or non-existent. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classification algorithm that provides accuracy comparable to other techniques whilst providing some insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. Clinicians are very familiar with weighted sum scoring scales so the internal representation is intuitive and easily understood. This paper presents results from the use of the AWSum approach with data from patients suffering from Cystic Fibrosis.
- Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John , Hafen, Gaudenz
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Third Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2009), Wellington, New Zealand : Vol. 97, p. 13-17
- Full Text:
- Description: Data mining is often performed with datasets associated with diseases in order to increase insights that can ultimately lead to improved prevention or treatment. Classification algorithms can achieve high levels of predictive accuracy but have limited application for facilitating the insight that leads to deeper understanding of aspects of the disease. This is because the representation of knowledge that arises from classification algorithms is too opaque, too complex or too sparse to facilitate insight. Clustering, association and visualisation approaches enable greater scope for clinicians to be engaged in a way that leads to insight, however predictive accuracy is compromised or non-existent. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classification algorithm that provides accuracy comparable to other techniques whilst providing some insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. Clinicians are very familiar with weighted sum scoring scales so the internal representation is intuitive and easily understood. This paper presents results from the use of the AWSum approach with data from patients suffering from Cystic Fibrosis.
A comparison of machine learning algorithms for multilabel classification of CAN
- Kelarev, Andrei, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert
- Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Journal article
- Relation: Advances in Computer Science and Engineering Vol. 9, no. 1 (2012), p. 1-4
- Full Text:
- Reviewed:
- Description: This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.
- Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Journal article
- Relation: Advances in Computer Science and Engineering Vol. 9, no. 1 (2012), p. 1-4
- Full Text:
- Reviewed:
- Description: This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.
A count data model for heart rate variability forecasting and premature ventricular contraction detection
- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki, Jelinek, Herbert
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Journal article
- Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
- Full Text:
- Reviewed:
- Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Journal article
- Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
- Full Text:
- Reviewed:
- Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
A global optimisation approach to classification in medical diagnosis and prognosis
- Bagirov, Adil, Rubinov, Alex, Yearwood, John, Stranieri, Andrew
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
A lightweight blockchain based framework for underwater ioT
- Uddin, Md, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Md , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Uddin, Md , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
A model for the introduction of Ayurvedic and Allopathic Electronic Health Records in Sri Lanka
- Stranieri, Andrew, Sahama, Tony, Butler-Henderson, Kerryn, Perera, Kamal
- Authors: Stranieri, Andrew , Sahama, Tony , Butler-Henderson, Kerryn , Perera, Kamal
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 IEEE International Symposium on Technology and Society; Trivandrum, Kerala, India; 20th-22nd October 2016 p. 56-61
- Full Text:
- Reviewed:
- Description: Fully integrated electronic health records (EHR) provide healthcare providers and patients access to records across a health care system and promise efficient and effective provision of health care. However, fully integrated records have proven to be very expensive and difficult to establish. Currently. EHR's have been developed largely to accommodate Western medicine events. These barriers impact on the introduction of EHR's in Sri Lanka, where health budgets are already stretched and Ayurvedic medicine is routinely practiced alongside Allopathic medicine. This article identifies requirements for EHR in the Sri Lankan context and advances a model for the introduction of EHR's that suits that context. The model is justified by drawing on insights and experiences with EHR in Western nations.
- Authors: Stranieri, Andrew , Sahama, Tony , Butler-Henderson, Kerryn , Perera, Kamal
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 IEEE International Symposium on Technology and Society; Trivandrum, Kerala, India; 20th-22nd October 2016 p. 56-61
- Full Text:
- Reviewed:
- Description: Fully integrated electronic health records (EHR) provide healthcare providers and patients access to records across a health care system and promise efficient and effective provision of health care. However, fully integrated records have proven to be very expensive and difficult to establish. Currently. EHR's have been developed largely to accommodate Western medicine events. These barriers impact on the introduction of EHR's in Sri Lanka, where health budgets are already stretched and Ayurvedic medicine is routinely practiced alongside Allopathic medicine. This article identifies requirements for EHR in the Sri Lankan context and advances a model for the introduction of EHR's that suits that context. The model is justified by drawing on insights and experiences with EHR in Western nations.
A scalable cloud Platform for Active healthcare monitoring applications
- Balasubramanian, Venki, Stranieri, Andrew
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2015
- Type: Text , Conference paper
- Relation: 2014 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2014; Melbourne, Australia; 10th-12th December 2014 p. 93-98
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a real-time-like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on monitoring requirements from the health care providers, and are aligned with scalable economic models. © 2014 IEEE.
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2015
- Type: Text , Conference paper
- Relation: 2014 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2014; Melbourne, Australia; 10th-12th December 2014 p. 93-98
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a real-time-like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on monitoring requirements from the health care providers, and are aligned with scalable economic models. © 2014 IEEE.
A systematic literature review on the evaluation of business simulation games using PRISMA
- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Conference paper
- Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
- Full Text:
- Reviewed:
- Description: In recent years, organisational software process education has seen a considerable uptick in interest in adopting business simulation games (BSGs) as a novel learning resource. However, the lack of reliable and valid instruments to evaluate simulation learning outcomes inhibits the adoption and progress of simulation in Information System education. To fill this need, we performed a systematic review of 33 empirical studies using the PRISMA declaration approach to identify the different evaluation methods used to analyse BSG learning outcomes. We created a concept matrix using a didactic framework that categorised these assessment methodologies into three game stages (pre-game, in-game and post-game). We established a comprehensive evaluation strategy using this concept matrix, which teachers and researchers may use to choose the best appropriate evaluation method to analyse a wide range of learning outcomes of business simulation games. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Conference paper
- Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
- Full Text:
- Reviewed:
- Description: In recent years, organisational software process education has seen a considerable uptick in interest in adopting business simulation games (BSGs) as a novel learning resource. However, the lack of reliable and valid instruments to evaluate simulation learning outcomes inhibits the adoption and progress of simulation in Information System education. To fill this need, we performed a systematic review of 33 empirical studies using the PRISMA declaration approach to identify the different evaluation methods used to analyse BSG learning outcomes. We created a concept matrix using a didactic framework that categorised these assessment methodologies into three game stages (pre-game, in-game and post-game). We established a comprehensive evaluation strategy using this concept matrix, which teachers and researchers may use to choose the best appropriate evaluation method to analyse a wide range of learning outcomes of business simulation games. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.
A web-based Narrative construction environment
- Yearwood, John, Stranieri, Andrew, Osman, Deanna
- Authors: Yearwood, John , Stranieri, Andrew , Osman, Deanna
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at NILE 2008: 5th International Conference on Narrative and Interactive Learning Environments, Edinburgh, Scotland : 6th-8th August 2008 p. 78-81
- Full Text:
- Description: This paper describes a web-based environment for constructing narrative from story snippets contributed by a community of interest. The underlying model uses an argument based structure to infer the next event in the narrative sequence. The approach makes use of both events and higher level story elements derived from Polti’s dramatic situations. Dramatic situations used are consistent with a theme, and events are generally constrained by the dramatic situation. The narrative generated is a function of the event history, the dramatic situations chosen and the plausible inferences about next events that are contributed by a community of interest in the theme. At this stage, a player’s actions are simulated using a random selection from a set and the implementation of a nonsense filter. Example outputs from the system are provided and discussed.
- Description: 2003006499
- Authors: Yearwood, John , Stranieri, Andrew , Osman, Deanna
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at NILE 2008: 5th International Conference on Narrative and Interactive Learning Environments, Edinburgh, Scotland : 6th-8th August 2008 p. 78-81
- Full Text:
- Description: This paper describes a web-based environment for constructing narrative from story snippets contributed by a community of interest. The underlying model uses an argument based structure to infer the next event in the narrative sequence. The approach makes use of both events and higher level story elements derived from Polti’s dramatic situations. Dramatic situations used are consistent with a theme, and events are generally constrained by the dramatic situation. The narrative generated is a function of the event history, the dramatic situations chosen and the plausible inferences about next events that are contributed by a community of interest in the theme. At this stage, a player’s actions are simulated using a random selection from a set and the implementation of a nonsense filter. Example outputs from the system are provided and discussed.
- Description: 2003006499
Addressing the complexities of big data analytics in healthcare : The diabetes screening case
- De Silva, Daswin, Burstein, Frada, Jelinek, Herbert, Stranieri, Andrew
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy
- Stranieri, Andrew, Abawajy, Jemal, Kelarev, Andrei, Huda, Shamsul, Chowdhury, Morshed, Jelinek, Herbert
- Authors: Stranieri, Andrew , Abawajy, Jemal , Kelarev, Andrei , Huda, Shamsul , Chowdhury, Morshed , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Artificial Intelligence in Medicine Vol. 58, no. 3 (2013), p. 185-193
- Full Text:
- Reviewed:
- Description: Objective: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN) We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery This is important as not all five Ewing tests can always be applied in each situation in practice Methods and material: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests Results: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery We found the best sequences of tests for cost-function equal to the number of tests The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93 They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained Conclusions: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence © 2013 Elsevier B.V.
- Description: 2003011130
- Authors: Stranieri, Andrew , Abawajy, Jemal , Kelarev, Andrei , Huda, Shamsul , Chowdhury, Morshed , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Artificial Intelligence in Medicine Vol. 58, no. 3 (2013), p. 185-193
- Full Text:
- Reviewed:
- Description: Objective: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN) We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery This is important as not all five Ewing tests can always be applied in each situation in practice Methods and material: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests Results: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery We found the best sequences of tests for cost-function equal to the number of tests The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93 They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained Conclusions: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence © 2013 Elsevier B.V.
- Description: 2003011130
An argument structure abstraction for Bayesian belief networks: Just outcomes in on-line dispute resolution
- Muecke, Nial, Stranieri, Andrew
- Authors: Muecke, Nial , Stranieri, Andrew
- Date: 2008
- Type: Text , Conference proceedings
- Full Text:
- Description: There are many different approaches for settling disputes on-line, such as simple email systems, fixed bid systems and intelligent systems. However, to date there have been no attempts to integrate decision support methods into the dispute resolution process for the purpose of supporting outcomes that are consistent with judicial reasoning. This paper describes how a model of judicial reasoning can be used to assist divorcees with the resolution of property issues online, in a manner that is consistent with decisions a judge would make if the matter was heard in Court. The approach uses an argument based model of the discretionary nature of decisions made by judges in Australian Family Law. This is integrated with a protocol for online dispute dialogue. Predictions of the likelihood of alternates outcomes is achieved with a series of Bayesian Belief Networks
- Authors: Muecke, Nial , Stranieri, Andrew
- Date: 2008
- Type: Text , Conference proceedings
- Full Text:
- Description: There are many different approaches for settling disputes on-line, such as simple email systems, fixed bid systems and intelligent systems. However, to date there have been no attempts to integrate decision support methods into the dispute resolution process for the purpose of supporting outcomes that are consistent with judicial reasoning. This paper describes how a model of judicial reasoning can be used to assist divorcees with the resolution of property issues online, in a manner that is consistent with decisions a judge would make if the matter was heard in Court. The approach uses an argument based model of the discretionary nature of decisions made by judges in Australian Family Law. This is integrated with a protocol for online dispute dialogue. Predictions of the likelihood of alternates outcomes is achieved with a series of Bayesian Belief Networks
An efficient selective miner consensus protocol in blockchain oriented iot smart monitoring
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
- Full Text:
- Reviewed:
- Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
- Full Text:
- Reviewed:
- Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
An intelligent learning environment for traditional Chinese medicine practitioners and students
- Jia, Long, Stranieri, Andrew, Shen, J
- Authors: Jia, Long , Stranieri, Andrew , Shen, J
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at HIC 2008 Australia's Health Informatics Conference; The Person in the Centre, Brunswick East, Victoria : 31st August - 2nd September 2008
- Full Text:
- Description: Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
- Description: 2003006755
- Authors: Jia, Long , Stranieri, Andrew , Shen, J
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at HIC 2008 Australia's Health Informatics Conference; The Person in the Centre, Brunswick East, Victoria : 31st August - 2nd September 2008
- Full Text:
- Description: Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
- Description: 2003006755
An interaction framework for scenario-based three dimensional environments
- Macfadyen, Alyx, Stranieri, Andrew, Yearwood, John
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at IE 2006, the 3rd Australasian Conference on Interactive Entertainment, Perth : 4th December, 2006
- Full Text:
- Reviewed:
- Description: Although popular and engaging, three dimensional environments are rarely deployed to depict strong narratives involving complex characters engaged in reasoning. The design of three dimensional environments rich in narrative and character depth can be facilitated with a detailed representation of interactions between characters. However, the representation of interaction in current 3D development environments such as game engines is quite basic. This work advances a scheme for representing interactions that integrates a representation of semantics from linguistics called FrameNet with conceptualizations of drama and narrative by Georges Polti and Joseph Campbell. The resulting interaction frame facilitates the design of 3D environments by providing designers rich, yet standard elements that include spatial and temporal data, with which to represent complex interactions in 3D environments. This has application for the authoring of dynamically generated interactive narrative environments.
- Description: E1
- Description: 2003001839
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at IE 2006, the 3rd Australasian Conference on Interactive Entertainment, Perth : 4th December, 2006
- Full Text:
- Reviewed:
- Description: Although popular and engaging, three dimensional environments are rarely deployed to depict strong narratives involving complex characters engaged in reasoning. The design of three dimensional environments rich in narrative and character depth can be facilitated with a detailed representation of interactions between characters. However, the representation of interaction in current 3D development environments such as game engines is quite basic. This work advances a scheme for representing interactions that integrates a representation of semantics from linguistics called FrameNet with conceptualizations of drama and narrative by Georges Polti and Joseph Campbell. The resulting interaction frame facilitates the design of 3D environments by providing designers rich, yet standard elements that include spatial and temporal data, with which to represent complex interactions in 3D environments. This has application for the authoring of dynamically generated interactive narrative environments.
- Description: E1
- Description: 2003001839
AppA : Assistive patient monitoring cloud platform for active healthcare applications
- Balasubramanian, Venki, Stranieri, Andrew, Kaur, Ranjit
- Authors: Balasubramanian, Venki , Stranieri, Andrew , Kaur, Ranjit
- Date: 2015
- Type: Text , Conference paper
- Relation: 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015; Bali, Indonesia; 8th-10th January 2015
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a realtime- like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on the monitoring requirements from the health care providers, and is aligned with scalable economic models.
- Authors: Balasubramanian, Venki , Stranieri, Andrew , Kaur, Ranjit
- Date: 2015
- Type: Text , Conference paper
- Relation: 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015; Bali, Indonesia; 8th-10th January 2015
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a realtime- like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on the monitoring requirements from the health care providers, and is aligned with scalable economic models.
Are ERP simulation games assisting students to be job-ready? An Australian universities’ perspective
- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference paper
- Relation: 30th Australiasian Conference on Information Systems (ACIS), 9-11 December 2019, Perth, Australia
- Full Text:
- Reviewed:
- Description: Deep and rapid changes in digital enterprise technology exceed the ability of traditional teaching methods to prepare students for challenges encountered in modern enterprises. Researchers proposed different pedagogical approaches to teach ERP (Enterprise Resource Planning) concepts such as ERPsim games to enhance students’ learning and job-readiness. Although the ERPsim studies verified the role of these games in enhancing students’ learning, whether these games contribute to student’s job readiness still needs to be explored. Using the mixed-method approach, this research-in-progress is designed to fill this gap by investigating the role of ERPsim game in increasing skills, learning levels, and job-readiness among university students in Australia. The findings from this study can contribute to the improvement of ERP pedagogical techniques. In addition, this research-in-progress will provide a concrete mapping to align learning outcomes/skills with ICT industry competencies standards as defined in SFIA (Skills framework for Information Age) and AQF (Australian Qualifications Framework).
Are ERP simulation games assisting students to be job-ready? An Australian universities’ perspective
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference paper
- Relation: 30th Australiasian Conference on Information Systems (ACIS), 9-11 December 2019, Perth, Australia
- Full Text:
- Reviewed:
- Description: Deep and rapid changes in digital enterprise technology exceed the ability of traditional teaching methods to prepare students for challenges encountered in modern enterprises. Researchers proposed different pedagogical approaches to teach ERP (Enterprise Resource Planning) concepts such as ERPsim games to enhance students’ learning and job-readiness. Although the ERPsim studies verified the role of these games in enhancing students’ learning, whether these games contribute to student’s job readiness still needs to be explored. Using the mixed-method approach, this research-in-progress is designed to fill this gap by investigating the role of ERPsim game in increasing skills, learning levels, and job-readiness among university students in Australia. The findings from this study can contribute to the improvement of ERP pedagogical techniques. In addition, this research-in-progress will provide a concrete mapping to align learning outcomes/skills with ICT industry competencies standards as defined in SFIA (Skills framework for Information Age) and AQF (Australian Qualifications Framework).
Argumentation structures that integrate dialectical and non-dialectical reasoning
- Stranieri, Andrew, Zeleznikow, John, Yearwood, John
- Authors: Stranieri, Andrew , Zeleznikow, John , Yearwood, John
- Date: 2001
- Type: Text , Journal article
- Relation: Knowledge Engineering Review Vol. 16, no. 4 (Dec 2001), p. 331-348
- Full Text:
- Reviewed:
- Description: Argumentation concepts have been applied to numerous knowledge engineering endeavours in recent years. For example, a variety of logics have been developed to represent argumentation in the context of a dialectical situation such as a dialogue. In contrast to the dialectical approach, argumentation has also been used to structure knowledge. This can be seen as a non-dialectical approach. The Toulmin argument structure has often been used to structure knowledge non-dialectically yet most studies that apply the Toulmin structure do not use the original structure but vary one or more components. Variations to the Toulmin structure can be understood as different ways to integrate a dialectical perspective with a non-dialectical one. Drawing the dialectical/non-dialectical distinction enables the specification of a framework called the generic actual argument model that is expressly non-dialectical. The framework enables the development of knowledge-based systems that integrate a variety of inference procedures, combine information retrieval with reasoning and facilitate automated document drafting. Furthermore, the non-dialectical framework provides the foundation for simple dialectical models. Systems based on our approach have been developed in family law, refugee law, determining eligibility for government legal aid, copyright law and e-tourism.
- Description: C1
- Description: 2003002516
- Authors: Stranieri, Andrew , Zeleznikow, John , Yearwood, John
- Date: 2001
- Type: Text , Journal article
- Relation: Knowledge Engineering Review Vol. 16, no. 4 (Dec 2001), p. 331-348
- Full Text:
- Reviewed:
- Description: Argumentation concepts have been applied to numerous knowledge engineering endeavours in recent years. For example, a variety of logics have been developed to represent argumentation in the context of a dialectical situation such as a dialogue. In contrast to the dialectical approach, argumentation has also been used to structure knowledge. This can be seen as a non-dialectical approach. The Toulmin argument structure has often been used to structure knowledge non-dialectically yet most studies that apply the Toulmin structure do not use the original structure but vary one or more components. Variations to the Toulmin structure can be understood as different ways to integrate a dialectical perspective with a non-dialectical one. Drawing the dialectical/non-dialectical distinction enables the specification of a framework called the generic actual argument model that is expressly non-dialectical. The framework enables the development of knowledge-based systems that integrate a variety of inference procedures, combine information retrieval with reasoning and facilitate automated document drafting. Furthermore, the non-dialectical framework provides the foundation for simple dialectical models. Systems based on our approach have been developed in family law, refugee law, determining eligibility for government legal aid, copyright law and e-tourism.
- Description: C1
- Description: 2003002516
Automatic sleep stage identification: difficulties and possible solutions
- Sukhorukova, Nadezda, Stranieri, Andrew, Ofoghi, Bahadorreza, Vamplew, Peter, Saleem, Muhammad Saad, Ma, Liping, Ugon, Adrien, Ugon, Julien, Muecke, Nial, Amiel, Hélène, Philippe, Carole, Bani-Mustafa, Ahmed, Huda, Shamsul, Bertoli, Marcello, Levy, P, Ganascia, J.G
- Authors: Sukhorukova, Nadezda , Stranieri, Andrew , Ofoghi, Bahadorreza , Vamplew, Peter , Saleem, Muhammad Saad , Ma, Liping , Ugon, Adrien , Ugon, Julien , Muecke, Nial , Amiel, Hélène , Philippe, Carole , Bani-Mustafa, Ahmed , Huda, Shamsul , Bertoli, Marcello , Levy, P , Ganascia, J.G
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.
- Authors: Sukhorukova, Nadezda , Stranieri, Andrew , Ofoghi, Bahadorreza , Vamplew, Peter , Saleem, Muhammad Saad , Ma, Liping , Ugon, Adrien , Ugon, Julien , Muecke, Nial , Amiel, Hélène , Philippe, Carole , Bani-Mustafa, Ahmed , Huda, Shamsul , Bertoli, Marcello , Levy, P , Ganascia, J.G
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.
AWSum - applying data mining in a health care scenario
- Quinn, Anthony, Jelinek, Herbert, Stranieri, Andrew, Yearwood, John
- Authors: Quinn, Anthony , Jelinek, Herbert , Stranieri, Andrew , Yearwood, John
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, Sydney, New South Wales : 15th-18th December 2008 p. 291-296
- Full Text:
- Description: This paper investigates the application of a new data mining algorithm called Automated Weighted Sum, (AWSum), to diabetes screening data to explore its use in providing researchers with new insight into the disease and secondarily to explore the potential the algorithm has for the generation of prognostic models for clinical use. There are many data mining classifiers that produce high levels of predictive accuracy but their application to health research and clinical applications is limited because they are complex, produce results that are difficult to interpret and are difficult to integrate with current knowledge and practises. This is because most focus on accuracy at the expense of informing the user as to the influences that lead to their classification results. By providing this information on influences a researcher can be pointed to new potentially interesting avenues for investigation. AWSum measures influence by calculating a weight for each feature value that represents its influence on a class value relative to other class values. The results produced, although on limited data, indicated the approach has potential uses for research and has some characteristics that may be useful in the future development of prognostic models.
- Description: 2003006660
- Authors: Quinn, Anthony , Jelinek, Herbert , Stranieri, Andrew , Yearwood, John
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, Sydney, New South Wales : 15th-18th December 2008 p. 291-296
- Full Text:
- Description: This paper investigates the application of a new data mining algorithm called Automated Weighted Sum, (AWSum), to diabetes screening data to explore its use in providing researchers with new insight into the disease and secondarily to explore the potential the algorithm has for the generation of prognostic models for clinical use. There are many data mining classifiers that produce high levels of predictive accuracy but their application to health research and clinical applications is limited because they are complex, produce results that are difficult to interpret and are difficult to integrate with current knowledge and practises. This is because most focus on accuracy at the expense of informing the user as to the influences that lead to their classification results. By providing this information on influences a researcher can be pointed to new potentially interesting avenues for investigation. AWSum measures influence by calculating a weight for each feature value that represents its influence on a class value relative to other class values. The results produced, although on limited data, indicated the approach has potential uses for research and has some characteristics that may be useful in the future development of prognostic models.
- Description: 2003006660