Title: A support system for coronary artery disease detection using a deep dense neural network

Authors: Debabrata Swain; Santosh Kumar Pani

Addresses: School of Computer Engineering, K.I.I.T. University, Bhubaneswar – 751024, India ' School of Computer Engineering, K.I.I.T. University, Bhubaneswar – 751024, India

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%.

Keywords: support vector machine; Random forest; decision tree; coronary artery disease; deep dense neural; UCI repository; KNN; Naive Bayes; standard scaler; Relu activation function.

DOI: 10.1504/IJCSM.2022.128187

International Journal of Computing Science and Mathematics, 2022 Vol.16 No.3, pp.292 - 305

Received: 28 Aug 2020
Accepted: 09 Sep 2020
Published online: 11 Jan 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article