A Data Mining Approach for Dyslipidemia Disease Prediction Using Carotid Arterial Feature Vectors

摘要

In this paper,we proposed a useful methodology for the diagnosis of dyslipidemia disease by using novel various features of carotid arterial wall thickness. We measured and tested intima-media thickness of carotid arteries and used them as diagnostic feature vectors. In order to evaluate extracted various features,we tested on five classification methods and evaluated performance of classifiers. As a result,SVM and Neural Network algorithms (about 92%-98% goodness of fit) outperformed the other classifiers on those selected features.