Coronary artery disease prediction based on optimal feature selection using improved artificial neural network with meta-heuristic algorithm

D.VETRITHANGAM; V. SENTHILKUMAR; A. RAMESH KUMAR; P.NARESH KUMAR; MRADULA SHARMA; NEHA

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Coronary artery disease prediction based on optimal feature selection using improved artificial neural network with meta-heuristic algorithm

Author(s)

D.VETRITHANGAM Department of Computer Science & Engineering , Chandigarh University, Punjab, India

V. SENTHILKUMAR Department of Mechanical Engineering, SRM TRP Engineering College, Tamilnadu, India

A. RAMESH KUMAR Department of Mechatronics Engineering, K.S. Rangasamy College of Technology, Tamil Nadu, India

P.NARESH KUMAR Department of Computer Science and Engineering, KG Reddy College of Engineering and Technology, Telangana, India

MRADULA SHARMA Assistant Professor, Department of Computer Science and Information Technology, Japyee Institute of Information Technology, Uttarpradesh, India

NEHA Department of Computer Science & Engineering ,Chandigarh University , Punjab, India

Abstract

Scientific breakthroughs in understanding the etiology of coronary artery disease (CAD) will allow for more accurate coronary artery disease (CAD) diagnosis and treatment techniques. Coronary Artery Disease (CAD) is a type of cardiovascular disease in which atherosclerotic plaques in the coronary arteries cause myocardial infarction or sudden cardiac death. In medicine, disease prediction based on Artificial Neural Networks (ANN) plays a significant role in enhancing the reliability of general population health care. So, our main goal is to propose an improved artificial neural network model in conjunction with a Metaheuristic algorithm that works with distinct types of CAD datasets with good accuracy. The system selects the most relevant or similar features from the raw dataset; this feature selection is achieved by the Metaheuristic algorithm. This model uses 18 input nodes, 18 hidden nodes, and 1 output node in an 18-18-1 multilayered feed-forward network architecture, which is the best network for the prediction of CAD with the selected dataset. When using different methodologies on datasets dealing with coronary artery disease (CAD), the results may vary. Efficient medical diagnosis and analysis are important in selecting the important features. The Cleveland Heart Disease dataset, obtained from the UCI repository, was used in this paper; it contains 37079 person data records with 50 attributes. The proposed Improved Artificial Neural Network model with Meta-heuristic Algorithm results in 97.63% Sensitivity, 97.5% Accuracy, and 97.35 % Specificity.

Publication Details

Page(s) 4771-4782

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 24, Year: 2022

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Keywords

deep learning, Coronary artery disease, Risk Factor Meta-Heuristic Algorithm And Artificial Neural Networks.

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