标题： 识别易感人群中的心肌梗死风险：一种机器学习方法 显示英文标题

标题： Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach

摘要： COVID-19大流行显著增加了40岁以上人群感染后心血管事件，尤其是心肌梗死的发病率。尽管潜在机制尚不清楚，本研究采用混合机器学习方法分析流行病学数据，评估13个关键的心脏病风险因素及其易感性。基于一个独特的数据集，该数据集结合了人口统计学、生物化学、心电图和铊应激测试，本研究针对不同的风险状况分类出不同的亚群体，然后使用聚类算法将人群分为‘有风险’（AR）和‘无风险’（NAR）组。研究表明，在所研究的13个风险因素中，心脏病发作的可能性之间存在强关联。绝经后患者的加剧风险表明由于雌激素减少导致个体风险因素受损，这可能进一步因外在压力影响，如焦虑和恐惧而恶化，这些方面传统上使数据建模预测难以实现。 显示英文摘要

摘要： The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular events, particularly myocardial infarction, in individuals over 40. While the underlying mechanisms remain elusive, this study employs a hybrid machine learning approach to analyze epidemiological data in assessing 13 key heart attack risk factors and their susceptibility. Based on a unique dataset that combines demographic, biochemical, ECG, and thallium stress-tests, this study categorizes distinct subpopulations against varying risk profiles and then divides the population into 'at-risk' (AR) and 'not-at-risk' (NAR) groups using clustering algorithms. The study reveals strong association between the likelihood of experiencing a heart attack on the 13 risk factors studied. The aggravated risk for postmenopausal patients indicates compromised individual risk factors due to estrogen depletion that may be, further compromised by extraneous stress impacts, like anxiety and fear, aspects that have traditionally eluded data modeling predictions.