Title: Coronary artery disease classification from clinical heart disease features using deep neural network
Authors: D. Rajeswari; K. Thangavel
Addresses: Department of Computer Science, Periyar University, Salem, 636 011, India ' Department of Computer Science, Periyar University, Salem, 636 011, India
Abstract: Coronary artery disease (CAD) is the most dreadful clinical syndrome affecting a multitude of people globally and it increases the morbidity rate every year. Early detection of CAD is very important for appropriate treatment which can stop complications like heart failure. The clinical health data can effectively be used for the non-invasive detection of CAD. In this work, we employ deep neural network (DNN) for developing a heart disease prediction model. The proposed model has been tested on ZAlizadeh Sani dataset from UCI and the results show that the DNN classifier improves prediction accuracy significantly. The performance improvement of 75.7% using DNN architecture has been achieved when compared to K-nearest neighbour (KNN).
Keywords: CAD; coronary artery disease; heart disease; data mining; machine learning; deep learning; DNN; deep neural network; KNN; classification.
International Journal of Dynamical Systems and Differential Equations, 2022 Vol.12 No.2, pp.200 - 214
Accepted: 10 Jul 2020
Published online: 20 Jun 2022 *