Utilizing AI Modeling in conjunction with Deep Learning for the Prediction of Myocardial Infarction

Abstract

Cardiovascular diseases, especially myocardial infarctions, remain a leading cause of mortality worldwide. The early identification and accurate forecasting of arrhythmias might significantly improve outcomes for ill individuals. The research introduces a sophisticated model of an Artificial Intelligence and deep learning-based Intelligent Analytical Arrhythmia Predictor for forecasting myocardial infarction. It consists of a robust amalgamation of Convolutional Neural Networks, Levenberg-Marquardt Neural Networks, and decision trees, hence enhancing classification accuracy using ECG and MRI data. The suggested approach has a classification accuracy of 99.5% for arrhythmia prediction and a detection accuracy of 96% for cardiac scar volume. The approach comprises five phases, including ECG signal preprocessing, MRI scar recognition, feature extraction, and AI-driven classification algorithms. The IAAP model forecasts myocardial scar volume, a critical factor in myocardial infarction risk assessment. The findings demonstrate that the deep learning methodologies used in this research provide superior accuracy compared to Support Vector Machines or K-Nearest Neighbors in predictive performance. The research demonstrates that the Binary Back Propagation Neural Network (BBP-LA) architecture achieves an accuracy of 88%-87% in the early prediction of cardiovascular disease and stroke. Artificial intelligence will facilitate the automation of arrhythmia detection, augmenting real-time cardiac monitoring and eventually improve diagnostic accuracy in clinical practice. The findings provide compelling evidence that this novel AI and deep learning methodology has the potential to transform the whole paradigm of myocardial infarction diagnosis. This may result in more immediate, accurate, and scalable diagnostic solutions for enhancing patient care