Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization.

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 fromthe 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 andmetaheuristics bio-inspired algorithmsuch 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 fromthe 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. [ABSTRACT FROM AUTHOR]