Showing items 81 - 100 of 169

Your selections:

Australia's under-utilised bioenergy resources

- Lang, Andrew, Kopetz, Heinz, Stranieri, Andrew, Parker, Albert

  • Authors: Lang, Andrew , Kopetz, Heinz , Stranieri, Andrew , Parker, Albert
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Waste and Biomass Valorization Vol. 5, no. 2 (2014), p. 235-243
  • Full Text: false
  • Reviewed:
  • Description: The potential for bioenergy in Australia is very large with up to 50 million tonnes a year of biomass residues and wastes being presently a greatly under-exploited resource. The renewable energy derived from these biomass forms to generate electricity and heat, and transport fuels would significantly improve Australia's energy security, boost its economy, and benefit the environment. This paper examines these presently neglected resources and discusses how they could be exploited with use of current mature technologies to reduce Australia's greenhouse gas emissions and heavy reliance on fossil fuels. © Springer Science+Business Media Dordrecht 2013.

Energy-efficient priority-based routing scheme for the healthcare wireless sensor networks

- Saeed, Ather, Stranieri, Andrew, Dazeley, Richard

  • Authors: Saeed, Ather , Stranieri, Andrew , Dazeley, Richard
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: 9th WSEAS International Conference on Remote Sensing, Budapest 10/12/2103 pg 19-27
  • Full Text: false
  • Reviewed:
  • Description: Abstract: - In time-critical and data intensive applications, efficient acquisition of sensitive datasets is a challenge because of network congestion, void regions and node failures that commonly occur in wireless sensor networks (WSN), while monitoring the wellbeing of patients with serious medical conditions. The sensor devices attached to such patients are used for monitoring the vital signs of those with serious heart problems, Parkinson disease, Epilepsy and high blood pressure. This paper typically focuses on the reliable acquisition of datasets and provides a fault-tolerant priority based routing scheme with Dynamic Jumping (FTMPR-DJ) for the energy-efficient acquisition and dissemination of datasets. A new fault-tolerant scheme has been proposed that will significantly minimize data loss and network congestion and is well supported with extensive experiments to show effectiveness of the proposed routing scheme.

Informatics to support patient choice between diverse medical systems C3 - 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services, Healthcom 2014

- Golden, Isaac, Stranieri, Andrew, Sahama, Tony, Pilapitiya, Senaka, Siribaddana, Sisira, Vaughan, Stephen

  • Authors: Golden, Isaac , Stranieri, Andrew , Sahama, Tony , Pilapitiya, Senaka , Siribaddana, Sisira , Vaughan, Stephen
  • Date: 2014
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Culturally, philosophically and religiously diverse medical systems including Western medicine, Traditional Chinese Medicine, Ayurvedic Medicine and Homeopathic Medicine, once situated in places and times relatively unconnected from each other, currently co-exist to a point where patients must choose which system to consult. These decisions require comparative analyses, yet the divergence in key underpinning assumptions is so great that comparisons cannot easily be made. However, diverse medical systems can be meaningfully juxtaposed for the purpose of making practical decisions if relevant information is presented appropriately. Information regarding privacy provisions inherent in the typical practice of each medical system is an important element in this juxtaposition. In this paper the information needs of patients making decisions regarding the selection of a medical system, are examined.

Novel data mining techniques for incompleted clinical data in diabetes management

- Jelinek, Herbert, Yatsko, Andrew, Stranieri, Andrew, Venkatraman, Sitalakshmi

  • Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi
  • Date: 2014
  • Type: Text , Journal article
  • Relation: British Journal of Applied Science & Technology Vol. 4, no. 33 (2014), p. 4591-4606
  • Relation: https://doi.org/10.9734/BJAST/2014/11744
  • Full Text:
  • Reviewed:
  • Description: An important part of health care involves upkeep and interpretation of medical databases containing patient records for clinical decision making, diagnosis and follow-up treatment. Missing clinical entries make it difficult to apply data mining algorithms for clinical decision support. This study demonstrates that higher predictive accuracy is possible using conventional data mining algorithms if missing values are dealt with appropriately. We propose a novel algorithm using a convolution of sub-problems to stage a super problem, where classes are defined by Cartesian Product of class values of the underlying problems, and Incomplete Information Dismissal and Data Completion techniques are applied for reducing features and imputing missing values. Predictive accuracies using Decision Branch, Nearest Neighborhood and Naïve Bayesian classifiers were compared to predict diabetes, cardiovascular disease and hypertension. Data is derived from Diabetes Screening Complications Research Initiative (DiScRi) conducted at a regional Australian university involving more than 2400 patient records with more than one hundred clinical risk factors (attributes). The results show substantial improvements in the accuracy achieved with each classifier for an effective diagnosis of diabetes, cardiovascular disease and hypertension as compared to those achieved without substituting missing values. The gain in improvement is 7% for diabetes, 21% for cardiovascular disease and 24% for hypertension, and our integrated novel approach has resulted in more than 90% accuracy for the diagnosis of any of the three conditions. This work advances data mining research towards achieving an integrated and holistic management of diabetes. - See more at: http://www.sciencedomain.org/abstract.php?iid=670&id=5&aid=6128#.VCSxDfmSx8E

Performance evaluation of the dependable properties of a body area wireless sensor network

- Balasubramanian, Venki, Stranieri, Andrew

  • Authors: Balasubramanian, Venki , Stranieri, Andrew
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: 2014 International Conference on Reliabilty, Optimization, & Information Technology (Icroit 2014); Faridabad, India; 6th-8th February 2014 p. 229-234
  • Full Text:
  • Reviewed:
  • Description: Body Area Wireless Sensor Networks (BAWSNs) are self-organizing networks capable of monitoring health intrinsic data of a patient. BAWSNs extended with a health care application can be used to perform medical assessments by remotely monitoring patients. The accuracy of medical assessments fundamentally depends on the correctness of the data received from the BAWSN. However, data errors may arise at the sensor or during transmission across the wireless sensor network. Therefore, it is imperative to measure the health intrinsic data of a patient precisely. The formulated measurable properties in our work precisely measure the performance of the BAWSN in a remote Healthcare Monitoring Application (HMA). In this paper, we collated various performances using the measurable properties in our real-time test-bed and presented a comprehensive evaluation of these properties in a BAWSN.

Real-time self-stabilizing scheme for the localization of faults in wireless sensor networks

- Saeed, Ather, Stranieri, Andrew, Dazeley, Richard

  • Authors: Saeed, Ather , Stranieri, Andrew , Dazeley, Richard
  • Date: 2014
  • Type: Text , Book chapter
  • Relation: Recents advances in Image, Audio and Signal Processing p. 233-242
  • Full Text: false
  • Reviewed:
  • Description: Reliable acquisition of data from massively dense wireless sensor networks (WSN) is a challenge due to the unpredictable behaviour of nodes responsible for collecting and disseminating datasets of interest. Therefore, accurate sensing of events from nodes depend on several microscopic and macroscopic factors such as distance of a node from the sink, radio signal strength and connectedness of network for routing datasets to the nearest sink. Several Clustering schemes have been proposed for routing datasets, where major focus was on finding the next cluster-head with maximum energy for routing data. Such schemes are not suitable for the real-time dissemination of datasets because electing the next cluster-head is a computational intensive process. A new energy-efficient self-stabilizing sliding rectangle protocol (ESSRP) is proposed in this paper for ensuring reliability and connectedness of regions for minimizing data loss and prolonging network life. The proposed scheme not only looks at the energy-balance of a particular cluster but also ensures fault-localization and tolerance by providing self-stabilization to network in the event of nodes or links failure using Green’s Theorem. The WSN rectangular regions should be oriented counter-clockwise, piecewise regular and continuously differentiable so that faults can be efficiently localized, identified and rectified in a particular region

Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football

- Jelinek, Herbert, Kelarev, Andrei, Robinson, Dean, Stranieri, Andrew, Cornforth, David

  • Authors: Jelinek, Herbert , Kelarev, Andrei , Robinson, Dean , Stranieri, Andrew , Cornforth, David
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Applied Soft Computing Vol. 14, no. PART A (2014), p. 81-87
  • Full Text: false
  • Reviewed:
  • Description: This work investigates the effectiveness of using computer-based machine learning regression algorithms and meta-regression methods to predict performance data for Australian football players based on parameters collected during daily physiological tests. Three experiments are described. The first uses all available data with a variety of regression techniques. The second uses a subset of features selected from the available data using the Random Forest method. The third used meta-regression with the selected feature subset. Our experiments demonstrate that feature selection and meta-regression methods improve the accuracy of predictions for match performance of Australian football players based on daily data of medical tests, compared to regression methods alone. Meta-regression methods and feature selection were able to obtain performance prediction outcomes with significant correlation coefficients. The best results were obtained by the additive regression based on isotonic regression for a set of most influential features selected by Random Forest. This model was able to predict athlete performance data with a correlation coefficient of 0.86 (p < 0.05). © 2013 Published by Elsevier B.V. All rights reserved.
  • Description: C1

Visual character N-grams for classification and retrieval of radiological images

- Kulkarni, Pradnya, Stranieri, Andrew, Kulkarni, Siddhivinayak, Ugon, Julien, Mittal, Manish

  • Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Siddhivinayak , Ugon, Julien , Mittal, Manish
  • Date: 2014
  • Type: Text , Journal article
  • Relation: International Journal of Multimedia & Its Applications Vol. 6, no. 2 (April 2014), p. 35-49
  • Full Text:
  • Reviewed:
  • Description: Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases.

A participatory information management framework for patient centred care of autism spectrum disorder

- De Silva, Daswin, Burstein, Frada, Stranieri, Andrew, Williams, Katrina, Rinehart, Nicole

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

Association of ankle brachial pressure index with heart rate variability in a rural screening clinic

- Jelinek, Herbert, De Silva, Daswin, Burstein, Frada, Stranieri, Andrew, Khalaf, Kinda, Khandoker, Ahsan, Al-Aubaidy, Hayder

Capped K-NN Editing in definition lacking environments

- Stranieri, Andrew, Yatsko, Andrew, Golden, Isaac, Mammadov, Musa, Bagirov, Adil

  • Authors: Stranieri, Andrew , Yatsko, Andrew , Golden, Isaac , Mammadov, Musa , Bagirov, Adil
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Journal of Pattern Recognition Research Vol. 8, no. 1 (2013), p. 39-58
  • Full Text: false
  • Reviewed:
  • Description: While any input may be contributing, imprecise specification of class of data subdivided into classes identifies as rather common a source of noise. The misrepresentation may be characteristic of the data or be caused by forcing of a regression problem into the classification type. Consideration is given to examples of this nature, and an alternative is proposed. In the main part, the approach is based on a well-known technique of data treatment for noise using k-NN. The paper advances an editing technique designed around idea of variable number of authenticating instances. Test runs performed on publicly available and proprietary data demonstrate high retention ability of the new procedure without loss of classification accuracy. Noise reduction methods in a broader classification context are extensively surveyed.

CWDM: A case-based diabetes management web system

- Nguyen, Linh Hoang, Sun, Zhaohao, Stranieri, Andrew, Firmin, Sally

  • Authors: Nguyen, Linh Hoang , Sun, Zhaohao , Stranieri, Andrew , Firmin, Sally
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 24th Australasian Conference on Information Systems, 4-6th December, 2013 p. 1-10
  • Full Text:
  • Reviewed:
  • Description: Treatment refers to the therapy to treat a disease or a health issue. Treatment in this situation is similar to medical treatment which mainly uses medicines in an attempt to relieve the pain or even stop the disease. However, medicines themselves could not entirely cure the disease (in this case, diabetes), the patients will need more intervention which will be introduced in the next section. In most of documents for diabetic treatment, insulin therapy may be the main factor, however it would seem that diabetic patient needs more than just insulin. Therefore, TCM – traditional Chinese medicine – is recommended in the diabetic treatment as a lot of its remedies not only adjust insulin but also maintain good health for the patients. This section presents some of the TCM remedies to treat diabetes. As mentioned, diabetic patients are treated by lifestyle intervention and insulin therapy according to their diabetic status. The prevalence of diabetes and its complications leads to the requirement of treatment and care plan. Guidelines for T2D treatment indicated the following primary areas: lifestyle improvement which involves at least two and half hours of physical operations every week, dietary plan which decreases the fat intake, and weight management which requires weight loss approximately 7% of the baseline weight; cardiovascular risk factor reduction by managing blood pressure, cholesterol level, control smoking status, hypertension; and blood glucose management such as mono-therapy methods using oral medications to reduce A1c levels (Ripsin, Kang, & Urban, 2009). Self-monitoring of blood glucose levels for T2D treatment is also suggested. The self-monitoring of blood glucose method is recommended because it could enhance the patients’ self-consciousness of managing their diabetic status and require greater behaviours, responsibilities and efforts. Besides, this method is cost-effective in long term for diabetic complications treatment (Szymborska-Kajaneka, Psureka, Heseb, & Strojek, 2009). Another related study recommended that for T2D patients who are using insulin, self-monitoring of blood glucose should be carried out daily at least three times; and for patients without insulin usage the frequency of blood glucose self-monitoring should be adjusted individually (Varanauskiene, 2008). Both studies indicate that there have been controversies whether self-monitoring of blood glucose is useful for T2D patients without insulin treatment. We recommend traditional Chinese medicine (TCM) as the major medicine for treating diabetes according to a report of natural Chinese medicines (Li, Zheng, Bukuru, & Kimpe, 2004) which indicates the results from many cases in various research and medical activities.

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

- Kelarev, Andrei, Abawajy, Jemal, Stranieri, Andrew, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Abawajy, Jemal , Stranieri, Andrew , Jelinek, Herbert
  • Date: 2013
  • Type: Text , Journal article
  • Relation: International Journal of Data Warehousing and mining Vol. 9, no. 4 (2013), p. 1-18
  • Full Text: false
  • Reviewed:
  • Description: Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Multivariate data-driven decision guidance for clinical scientists

- Burstein, Frada, De Silva, Daswin, Jelinek, Herbert, Stranieri, Andrew

  • Authors: Burstein, Frada , De Silva, Daswin , Jelinek, Herbert , Stranieri, Andrew
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 29th International Conference on Data Engineering Workshops, ICDEW 2013; Proceedings - International Conference on Data Engineering p. 193-199
  • Full Text:
  • Reviewed:
  • Description: Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management 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 created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.
  • Description: E1

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

- Abawajy, Jemal, Kelarev, Andrei, Chowdhury, Morshed, Stranieri, Andrew, Jelinek, Herbert

  • Authors: Abawajy, Jemal , Kelarev, Andrei , Chowdhury, Morshed , Stranieri, Andrew , Jelinek, Herbert
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Computers in Biology and Medicine Vol. 43, no. 10 (2013), p. 1328-1333
  • Full Text:
  • Reviewed:
  • Description: Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features. © 2013 Elsevier Ltd.
  • Description: C1

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.

Approaches for community decision making and collective reasoning: Knowledge technology support

- Yearwood, John, Stranieri, Andrew

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2012
  • Type: Text , Book
  • Relation: Approaches for Community Decision Making and Collective Reasoning: Knowledge Technology Support
  • Full Text: false
  • Reviewed:
  • Description: Technology currently encourages the capture and storage of vast quantities of data and information and so thinkers, reasoners, and decision-makers have available large resources to support their tasks. At the same time, there is a need to engage with an enormous range of complex issues that require reasoning and decisions that are actionable to address them. Approaches for Community Decision Making and Collective Reasoning: Knowledge Technology Support acts to provide knowledge for each individual in a group with the broad structural wealth of reasoning. It also acts as an explicit structure that technological devices for supporting reasoning within a group can hook onto. If you are interested in how groups can structure their activities towards making better decisions or in developing technologies for the support of decision-making in groups, then this book is an excellent way to understand the state of the art and possible ways forward.

Data mining Traditional Chinese Medicine (TCM) : Lessons learnt from mining in law and allopathic medicine

- Stranieri, Andrew, Sahama, Tony

  • Authors: Stranieri, Andrew , Sahama, Tony
  • Date: 2012
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Key decisions at the collection, pre-processing, transformation, mining and interpretation phase of any knowledge discovery from database (KDD) process depend heavily on assumptions and theoretical perspectives relating to the type of task to be performed and characteristics of data sourced. In this article, we compare and contrast theoretical perspectives and assumptions taken in data mining exercises in the legal domain with those adopted in data mining in TCM and allopathic medicine. The juxtaposition results in insights for the application of KDD for Traditional Chinese Medicine. © 2012 IEEE.
  • Description: 2003009797

Detection of CAN by ensemble classifiers based on Ripple Down rules

- Kelarev, Andrei, Dazeley, Richard, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Dazeley, Richard , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Book chapter
  • Relation: Knowledge Management and Acquisition for Intelligent Systems p. 147-159
  • Full Text: false
  • Reviewed:
  • Description: It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.