arXiv:2501.18893cs.LG2025-01

研究发现族裔是早发冠心病的重要预测因子,仅次于年龄和性别。

A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran

  • 通过特征权重分析,比较族裔与八种传统风险因素的重要性
  • 族裔在预测模型中排第三,加入后可提升诊断准确率
  • 为多族裔人群的冠心病预防提供个性化依据

早发冠状动脉疾病(PCAD)指男性55岁前、女性65岁前发病。冠状动脉供血受阻常由生活方式及心代谢因素导致,但族裔影响研究较少。本研究基于伊朗全国大样本,分析年龄、性别、体重指数(BMI)、内脏肥胖(腰围WC)、糖尿病(DM)、高血压(HBP)、高密度脂蛋白胆固醇(LDL-C)及吸烟等八项因素,结合族裔对PCAD的影响。所有符合年龄标准患者均经冠状动脉造影确诊。使用特征权重算法评估各因素重要性,并训练分类模型,在含或不含族裔信息下对比预测性能。结果显示,性别与年龄为最强预测因子,族裔居第三位;加入族裔信息可显著提升模型效率。研究证实族裔是预测PCAD的关键因素,应在诊疗与防控中予以重视。

原文摘要 · Abstract (English)

Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. Coronary Artery Disease (CAD) develops when coronary arteries, the major blood vessels supplying the heart with blood, oxygen, and nutrients, become clogged or diseased. This is often due to many risk factors, including lifestyle and cardiometabolic ones, but few studies were done on ethnicity as one of these risk factors, especially in PCAD. In this study, we tested the rank of ethnicity among the major risk factors of PCAD, including age, gender, body mass index (BMI), visceral obesity presented as waist circumference (WC), diabetes mellitus (DM), high blood pressure (HBP), high low-density lipoprotein cholesterol (LDL-C), and smoking in a large national sample of patients with PCAD from different ethnicities. All patients who met the age criteria underwent coronary angiography to confirm CAD diagnosis. The weight of ethnicity was compared to the other eight features using feature weighting algorithms in PCAD diagnosis. In addition, we conducted an experiment where we ran predictive models (classification algorithms) to predict PCAD. We compared the performance of these models under two conditions: we trained the classification algorithms, including or excluding ethnicity. This study analyzed various factors to determine their predictive power influencing PCAD prediction. Among these factors, gender and age were the most significant predictors, with ethnicity being the third most important. The results also showed that if ethnicity is used as one of the input risk factors for classification algorithms, it can improve their efficiency. Our results show that ethnicity ranks as an influential factor in predicting PCAD. Therefore, it needs to be addressed in the PCAD diagnostic and preventive measures.

冠心病机器学习族裔差异风险预测

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