Navigating the Effectiveness of Various M L Algorithms for Myocardial Infarction Prediction

A comprehensive analysis has been initiated using the ECG Heartbeat Categorization Dataset, a large collection derived from the PTB Diagnostic ECG Database and the MIT-BIH Arrhythmia Dataset. The primary objective of this project is to improve myocardial infarction (MI) early detection. To identify MI in ECG readings, a number of machine learning models—including unique deep neural network architectures—have been meticulously created and put into practice. The study emphasizes how machine learning can distinguish between heartbeats that are normal and those that are impacted by various arrhythmia, which will enhance diagnostics for cardiac health. Three distinct machine learning models have been compared indepth in order to evaluate each model’s efficacy. The main aim of this study is to demonstrate how artificial intelligence can be of great significance in early MI detection. The comprehensive evaluation of model efficacy conducted in this research is expected to have a significant influence on future developments in the field of cardiac health diagnosis.