Heart Disease Prediction based on Convolutional Neural Network Feature Genetic Algorithm Solutions

Abstract

Heart disease is a serious worldwide health issue that requires precise and effective diagnostic techniques for early identification and prevention. In order to improve the prediction of cardiac illness, this research investigates a hybrid strategy that combines genetic algorithms (GAs) with convolutional neural networks (CNNs). CNNs are used to successfully model hierarchical patterns in order to extract complicated characteristics from medical data. GAs are included into the system to enhance feature selection and boost prediction accuracy, improving CNN performance using evolutionary optimisation methods. Standard cardiac disease datasets are used to test the suggested model, and its effectiveness is shown by evaluating measures like accuracy, precision, recall, and computing economy. According to the results, the CNN-GA hybrid strategy performs better than conventional techniques and offers a reliable, scalable solution for the prediction of heart disease. This research demonstrates how combining evolutionary algorithms with machine learning might enhance AI-driven healthcare diagnoses and enhance patient outcomes worldwide.