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Coronary artery heart disease prediction: A comparative study of computational intelligence techniques

journal contribution
posted on 22.06.2021, 22:33 authored by Safial I Ayon, Md Milon Islam, Md Rahat HossainMd Rahat Hossain
Diseases is an unusual circumstance that affects single or more parts of a human’s body. Because of lifestyle and patrimonial, different kinds of disease are increasing day by day. Among all those diseases, heart disease turns out to be the most common disease and the impact of this ailment is dangerous than all other diseases. In this paper, we compared a number of computational intelligence techniques for the prediction of coronary artery heart disease. Seven computational intelligence techniques named as Logistic Regression (LR), Support Vector Machine (SVM), Deep Neural Network (DNN), Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), and K-Nearest Neighbor (K-NN) were applied and a comparative study was drawn. The performance of each technique was evaluated using Statlog and Cleveland heart disease dataset which are retrieved from the UCI machine learning repository database with several evaluation techniques. From the study, it can be carried out that the highest accuracy of 98.15% obtained by deep neural network with sensitivity and precision 98.67% and 98.01% respectively. The outcomes of the study were compared with the outcomes of the state of the art focusing on heart disease prediction that outperforms the previous study.

History

Start Page

1

End Page

20

Number of Pages

20

eISSN

0974-780X

ISSN

0377-2063

Publisher

Institution of Electronics and Telecommunication Engineers

Language

en

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Khulna University of Engineering & Technology, Bangladesh

Era Eligible

Yes

Journal

IETE Journal of Research