Abstract—According to WHO (World Health Organization), 17.9 million people die every year because of Cardiovascular diseases (CVDs) and it accounts for 31% of all global deaths. Hence, the diagnosis of such common and deadly diseases are becoming very critical. In this study, we proposed a method that uses Fisher's Linear Discriminant Analysis (FLDA) for dimension reduction and Artificial Neural Network (ANN) for obtaining an efficient CVD diagnostic model. Performance evaluations based on publicly available datasets show that the accuracy of the proposed method is 97%, which is superior than the state of the art.

Index Terms—Cardiovascular heart disease, cardiovascular artery disease, machine learning, artificial neural network, linear discriminant analysis, multilayer perceptron neural network.

Hilal Hacilar, Burcu Bakir-Gungor, Zafer Aydin, Vehbi Cagri Gungor are with Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey (email: hilal.hacilar@agu.edu.tr).
Yasin Gormez is with Department of Management Information System, Cumhuriyet University, Sivas, Turkey.
Cengiz Gezer is with Research & Development Manager in adesso Turkey.

Cite: Hilal Hacilar, Yasin Gormez, Burcu Bakir-Gungor, Cengiz Gezer, Zafer Aydin, Vehbi Cagri Gungor, "Classification of Cardiovascular Artery Diseases Using Artificial Neural Network," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 10, no. 1, pp. 34-41, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).