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Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization

P. Nandakumar, R. Subhashini*

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India

* Corresponding Author: R. Subhashini. Email: email

Computer Systems Science and Engineering 2024, 48(1), 57-75.


Heart disease is a major cause of death for many people in the world. Each year the death rate of people affected with heart disease increased a lot. Machine learning models have been widely used for the prediction of heart disease from the different University of California Irvine (UCI) Machine Learning Repositories. But, due to certain data, it predicts less accurately, whereas, for large data, its sub-model deep learning is used. Our literature work has identified that only traditional methods are used for the prediction of heart disease. It will produce less accuracy. To produce more efficacy, Euclidean Distance was used in this work for data pre-processing that will clean the unwanted data and metaheuristics bio-inspired algorithm such as elephant herding optimization (EHO) is utilized for feature selection. Then, this article proposes deep learning models such as convolutional neural network (CNN) and Inception-ResNet-v2 model for the prediction of heart disease from the benchmark dataset such as the UCI Cleveland heart dataset. Finally, the proposed hybrid model utilizes a convolutional neural network with an Inception-ResNet-v2 in the third layer of the architecture that classifies heart disease with the promising result of 98.77%, accuracy for the Cleveland dataset which outperforms all the other state-of-the-art methods. In future work, this model can be used to predict other diseases such as cancer, brain tumor and COVID-19 in available datasets for the betterment of human lives.


Cite This Article

P. Nandakumar and R. Subhashini, "Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization," Computer Systems Science and Engineering, vol. 48, no.1, pp. 57–75, 2024.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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