Machine Learning, Deep Learning Assisted Clinical Data Analysis on Myocardial Infarction

Abstract

Myocardial Infarction remains one of the leading causes of mortality worldwide, demanding accurate and timely diagnosis for effective clinical intervention. Traditional diagnostic approaches often rely on physician expertise, electrocardiogram interpretation, biomarker evaluation, and imaging results, which may delay rapid decision-making in emergency settings. Recent advancements in Deep Learning have demonstrated significant potential in transforming disease diagnosis through automated and data-driven analysis. This study presents a Deep Learning-assisted, clinical data-driven framework for the early detection and risk assessment of myocardial infarction using patient clinical records, laboratory parameters, and physiological indicators. The framework integrates data preprocessing, feature normalization, missing value imputation, and predictive modeling to improve diagnostic reliability. Several state-of-the-art Deep Learning models are evaluated for classification performance. These models are capable of learning complex nonlinear relationships among heterogeneous clinical variables and identifying hidden patterns associated with the occurrence of myocardial infarction. This paper discusses recent 10 years existing Machine Learning, Deep Learning methods and thereby guides further enhancements and improvements to state-of-the-art techniques. This research highlights the importance of intelligent healthcare analytics and demonstrates the Machine Learning and Deep Learning-assisted clinical decision systems in myocardial infarction diagnosis.