Coronary artery disease classification from clinical heart disease features using deep neural network
by D. Rajeswari; K. Thangavel
International Journal of Dynamical Systems and Differential Equations (IJDSDE), Vol. 12, No. 2, 2022

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).

Online publication date: Mon, 20-Jun-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Dynamical Systems and Differential Equations (IJDSDE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com