Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease

Kolukisa, BurakHacilar, HilalGoy, GokhanKus, MustafaBakir-Gungor, BurcuAral, Atilla)Gungor, Vehbi Cagri2021-05-202021-05-202018978-1-5386-5035-62639-1589https://hdl.handle.net/20.500.12573/730This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project no 3180177.According to the World Health Organization (WHO), 31% of the world's total deaths in 2016 (17.9 million) was due to cardiovascular diseases (CVD). With the development of information technologies, it has become possible to predict whether people have heart diseases or not by checking certain physical and biochemical values at a lower cost. In this study, we have evalated a set of different classification algorithms, linear discriminant analysis and proposed a new hybrid feature selection methodology for the diagnosis of coronary heart diseases (CHD). Throughout this research effort, using three publicly available Heart Disease diagnosis datasets (UCI Machine Learning Repository), we have conducted comparative performance evaluations in terms of accuracy, sensitivity, specificity, F-measure, AUC and running time.enginfo:eu-repo/semantics/closedAccessClassificationFeature SelectionLinear Discriminant AnalysisData MiningCardiovascular DiseaseEvaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery DiseaseconferenceObject