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Conference Paper
Assessment of the Classification Capability of Prediction Models for HRV and Carotid artery image analysis
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- Authors
- Heon Gyu Lee, Jin Hyoung Park, Keun Ho Ryu
- Issue Date
- 2012-08
- Citation
- International Conference on Frontiers of Information Technology, Applications and Tools (FITAT) 2012, pp.1-9
- Language
- English
- Type
- Conference Paper
- Abstract
- The main objective of our study is to evaluate the classification capabilities of prediction models and propose which are most likely to be suitable for clinical setting. We also develop and then suggest a novel methodology useful in developing the various features of carotid arterial wall thickness and heart rate variability helpful in diagnosing coronary heart disease. Various prediction models are applied in order to detect and extract those which provide the better differentiation between control and patient data. In our experiments, both heart rate variability and carotid features are used for building the prediction model. The prediction models are pattern-based prediction, function-based prediction and decision-tree induction method. As a result, function-based(SVM and Neural Networks) algorithms outperformed the other prediction models.
- KSP Keywords
- Carotid Artery, Decision Tree(DT), Image analysis, Induction method, Patient data, Pattern-based, Tree induction, arterial wall, coronary heart disease, function-based, heart rate variability