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

KOLUKISA B., Hacilar H., Goy G., Kus M., Bakir-Gungor B., Aral A., ...More

2018 IEEE International Conference on Big Data, Big Data 2018, Washington, United States Of America, 10 - 13 December 2018, pp.2232-2238 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/bigdata.2018.8622609
  • City: Washington
  • Country: United States Of America
  • Page Numbers: pp.2232-2238
  • Keywords: Cardiovascular Disease, Data Mining, Linear Discriminant Analysis, Feature Selection, Classification, HEART-DISEASE
  • Abdullah Gül University Affiliated: Yes

Abstract

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.