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Coronary artery heart disease prediction: A comparative study of computational intelligence techniques
journal contribution
posted on 2021-06-22, 22:33 authored by Safial I Ayon, Md Milon Islam, Md Rahat HossainMd Rahat HossainDiseases 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
1End Page
20Number of Pages
20eISSN
0974-780XISSN
0377-2063Publisher
Institution of Electronics and Telecommunication EngineersPublisher DOI
Language
enPeer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Khulna University of Engineering & Technology, BangladeshEra Eligible
- Yes