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Cardiovascular disease remains a leading cause of mortality in thecontemporary world. Its association with smoking, elevated blood pressure, andcholesterol levels underscores the significance of these risk factors. Thisstudy addresses the challenge of predicting myocardial illness, a formidabletask in medical research. Accurate predictions are pivotal for refininghealthcare strategies. This investigation conducts a comparative analysis ofsix distinct machine learning models: Logistic Regression, Support VectorMachine, Decision Tree, Bagging, XGBoost, and LightGBM. The attained outcomesexhibit promise, with accuracy rates as follows: Logistic Regression (81.00%),Support Vector Machine (75.01%), XGBoost (92.72%), LightGBM (90.60%), DecisionTree (82.30%), and Bagging (83.01%). Notably, XGBoost emerges as thetop-performing model. These findings underscore its potential to enhancepredictive precision for coronary infarction. As the prevalence ofcardiovascular risk factors persists, incorporating advanced machine learningtechniques holds the potential to refine proactive medical interventions.

Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction