A support system for coronary artery disease detection using a deep dense neural network Online publication date: Wed, 11-Jan-2023
by Debabrata Swain; Santosh Kumar Pani
International Journal of Computing Science and Mathematics (IJCSM), Vol. 16, No. 3, 2022
Abstract: Because of the advancement of present-day contraptions and hardware, human life has turned out to be very extravagant. Consequently physical endeavours for performing any work are lessening step by step. This leads an individual more inclined to coronary artery sickness which is an assortment of heart disorders. It has become the chief reason for mortality in the entire world. For a better and accurate identification of the disease, different researchers have explored many intelligent prediction systems. In this paper, an effective coronary artery disease prediction system is proposed using a deep dense neural network. The proposed model is an adaptive version of a dense neural network with the addition of deep hidden layer structure and dropout. Here, the data is collected from heart disease data sets present in the UCI repository. The classifier has shown a classification accuracy of 95.32%.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook