A data mining approach for dyslipidemia disease prediction using carotid arterial feature vectors

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.