Improving Cardiovascular Disease Prediction Through Comparative Analysis
  of Machine Learning Models: A Case Study on Myocardial Infarction
Jonayet Miah Duc M Ca Md Abu Sayed Ehsanur Rashid Lipu Fuad Mahmud S M Yasir Arafat
Cardiovascular disease remains a leading cause of mortality in the
contemporary world. Its association with smoking, elevated blood pressure, and
cholesterol levels underscores the significance of these risk factors. This
study addresses the challenge of predicting myocardial illness, a formidable
task in medical research. Accurate predictions are pivotal for refining
healthcare strategies. This investigation conducts a comparative analysis of
six distinct machine learning models: Logistic Regression, Support Vector
Machine, Decision Tree, Bagging, XGBoost, and LightGBM. The attained outcomes
exhibit promise, with accuracy rates as follows: Logistic Regression (81.00%),
Support Vector Machine (75.01%), XGBoost (92.72%), LightGBM (90.60%), Decision
Tree (82.30%), and Bagging (83.01%). Notably, XGBoost emerges as the
top-performing model. These findings underscore its potential to enhance
predictive precision for coronary infarction. As the prevalence of
cardiovascular risk factors persists, incorporating advanced machine learning
techniques holds the potential to refine proactive medical interventions.