C4.5: programs for machine learning
C4.5: programs for machine learning
Visualization and inference based on wavelet coefficients, SiZer and SiNos
Computational Statistics & Data Analysis
Automated classification reveals morphological factors associated with dementia
Applied Soft Computing
Artificial Intelligence in Medicine
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Hierarchically organized layout for visualization of biochemical pathways
Artificial Intelligence in Medicine
Decision support in heart failure through processing of electro- and echocardiograms
Artificial Intelligence in Medicine
A decision support system for cost-effective diagnosis
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction
Artificial Intelligence in Medicine
A semantic graph-based approach to biomedical summarisation
Artificial Intelligence in Medicine
Selection of effective features for ECG beat recognition based on nonlinear correlations
Artificial Intelligence in Medicine
Visually defining and querying consistent multi-granular clinical temporal abstractions
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Editorial: AI planning and scheduling in the medical hospital environment
Artificial Intelligence in Medicine
Continual planning and scheduling for managing patient tests in hospital laboratories
Artificial Intelligence in Medicine
Toward interactive scheduling systems for managing medical resources
Artificial Intelligence in Medicine
Information visualization and its application to medicine
Artificial Intelligence in Medicine
Metaphors of movement: a visualization and user interface for time-oriented, skeletal plans
Artificial Intelligence in Medicine
Acute leukemia classification by ensemble particle swarm model selection
Artificial Intelligence in Medicine
Statistical inference and visualization in scale-space using local likelihood
Computational Statistics & Data Analysis
Understanding risk factors in cardiac rehabilitation patients with random forests and decision trees
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Using decision tree for diagnosing heart disease patients
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Empirical study of bagging predictors on medical data
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
Computers in Biology and Medicine
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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.