C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Cardiovascular disease diagnosis method by emerging patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Emerging patterns based methodology for prediction of patients with myocardial ischemia
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Computer Methods and Programs in Biomedicine
Linear and nonlinear analysis of normal and CAD-affected heart rate signals
Computer Methods and Programs in Biomedicine
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The main purpose of our study is to propose a novel methodology to develop the multi-parametric feature including linear and nonlinear features of HRV (Heart Rate Variability) diagnosing cardiovascular disease. To develop the multi-parametric feature of HRV, we used the statistical and classification techniques. This study analyzes the linear and the non-linear properties of HRV for three recumbent positions, namely the supine, left lateral and right lateral position. Interaction effect between recumbent positions and groups (normal and patients) was observed based on the HRV indices and the extracted HRV indices used to classify the CAD (Coronary Artery Disease) group from the normal people. We have carried out various experiments on linear and non-linear features of HRV indices to evaluate several classifiers, e.g., Bayesian classifiers, CMAR, C4.5 and SVM. In our experiments, SVM outperformed the other classifiers.