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.

翻译：心血管疾病仍是当代世界的主要死亡原因。其与吸烟、高血压及胆固醇水平的关联凸显了这些风险因素的重要性。本研究聚焦于心肌疾病预测这一医学研究领域的重大挑战，精准预测对优化医疗策略具有关键意义。本项研究对六种机器学习模型进行了比较分析：逻辑回归、支持向量机、决策树、Bagging、XGBoost和LightGBM。实验结果表明：逻辑回归准确率为81.00%，支持向量机为75.01%，XGBoost为92.72%，LightGBM为90.60%，决策树为82.30%，Bagging为83.01%。值得注意的是，XGBoost展现出最优性能。这些发现突显了其在提升冠状动脉梗死预测精度方面的潜力。随着心血管风险因素持续存在，融合先进机器学习技术有望优化主动式医疗干预策略。