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
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- 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
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
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- 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
- 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
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- 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.
Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing features
- Abawajy, Jemal, Kelarev, Andrei, Stranieri, Andrew, Jelinek, Herbert
- Authors: Abawajy, Jemal , Kelarev, Andrei , Stranieri, Andrew , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This article is devoted to an empirical investigation of performance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using sub-sets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.
- Authors: Abawajy, Jemal , Kelarev, Andrei , Stranieri, Andrew , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This article is devoted to an empirical investigation of performance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using sub-sets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.
Rule-based classifiers and meta classifiers for identification of cardiac autonomic neuropathy progression
- Jelinek, Herbert, Kelarev, Andrei, Stranieri, Andrew, Yearwood, John
- Authors: Jelinek, Herbert , Kelarev, Andrei , Stranieri, Andrew , Yearwood, John
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Information Science and Computer Mathematics Vol. 5, no. 2 (2012), p. 49-53
- Full Text:
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- Description: We investigate and compare several rule-based classifiers and meta classifiers in their ability to obtain multi-class classifications of cardiac autonomic neuropathy (CAN) and its progression. The best results obtained in our experiments are significantly better than the outcomes published previously in the literature for analogous CAN identification tasks or simpler binary classification tasks.
- Authors: Jelinek, Herbert , Kelarev, Andrei , Stranieri, Andrew , Yearwood, John
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Information Science and Computer Mathematics Vol. 5, no. 2 (2012), p. 49-53
- Full Text:
- Reviewed:
- Description: We investigate and compare several rule-based classifiers and meta classifiers in their ability to obtain multi-class classifications of cardiac autonomic neuropathy (CAN) and its progression. The best results obtained in our experiments are significantly better than the outcomes published previously in the literature for analogous CAN identification tasks or simpler binary classification tasks.
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