用机器学习分析13个风险因素，识别40岁以上人群心梗高危群体。
Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach
- 融合人口、生化、心电图等多源数据，构建混合预测模型。
- 发现13个风险因素与心梗高度相关，尤其对绝经后女性风险加剧。
- 可帮助识别高危人群，适用于公共卫生筛查与早期干预。
新冠疫情期间，40岁以上人群感染后心血管事件（尤其是心肌梗死）发病率显著上升。尽管机制尚不明确，本研究采用混合机器学习方法分析流行病学数据，评估13个关键心梗风险因素及其易感性。基于包含人口统计、生化指标、心电图及铊负荷运动试验的独有数据集，研究将人群划分为不同风险谱型，并利用聚类算法区分高危（AR）与非高危（NAR）群体。结果揭示13个风险因素与心梗发生率存在强关联；绝经后患者风险加剧，可能源于雌激素缺失导致个体脆弱性增强，且焦虑、恐惧等外部应激因素进一步加重风险，这些传统上难以建模的变量在本研究中获得量化体现。
原文摘要 · Abstract (English)
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.
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