目的:在台湾队列中开发和验证用于预测冠状动脉疾病(CAD)的机器学习模型,重点是识别重要的预测因素,并比较各种模型的性能。
方法:本研究对临床、人口统计学,以及来自台湾生物银行(TWB)的8,495名受试者的实验室数据,经过倾向评分匹配以解决潜在的混杂因素。关键变量包括年龄,性别,血脂谱(T-CHO,HDL_C,LDL_C,TG),吸烟和饮酒习惯,以及肾功能和肝功能标志物。评估了多个机器学习模型的性能。
结果:该队列包括通过自我报告问卷确定的1,699名CAD患者。在人口统计学和临床特征方面,CAD和非CAD个体之间观察到显着差异。值得注意的是,梯度提升模型是最准确的,达到0.846的AUC(95%置信区间[CI]0.819-0.873),灵敏度为0.776(95%CI,0.732-0.820),特异性为0.759(95%CI,0.736-0.782),分别。准确性为0.762(95%CI,0.742-0.782)。年龄被确定为研究数据集中CAD风险的最有影响力的预测因子。
结论:梯度提升机器学习模型在台湾队列中预测CAD方面表现出卓越的性能,年龄是一个关键的预测指标。这些发现强调了机器学习模型在提高CAD预测精度方面的潜力。从而支持早期发现和有针对性的干预策略。
背景:不适用。
OBJECTIVE: To develop and validate machine learning models for predicting coronary artery disease (CAD) within a Taiwanese cohort, with an emphasis on identifying significant predictors and comparing the performance of various models.
METHODS: This study involved a comprehensive analysis of clinical, demographic, and laboratory data from 8,495 subjects in Taiwan Biobank (TWB) after propensity score matching to address potential confounding factors. Key variables included age, gender, lipid profiles (T-CHO, HDL_C, LDL_C, TG), smoking and alcohol consumption habits, and renal and liver function markers. The performance of multiple machine learning models was evaluated.
RESULTS: The cohort comprised 1,699 individuals with CAD identified through self-reported questionnaires. Significant differences were observed between CAD and non-CAD individuals regarding demographics and clinical features. Notably, the Gradient Boosting model emerged as the most accurate, achieving an AUC of 0.846 (95% confidence interval [CI] 0.819-0.873), sensitivity of 0.776 (95% CI, 0.732-0.820), and specificity of 0.759 (95% CI, 0.736-0.782), respectively. The accuracy was 0.762 (95% CI, 0.742-0.782). Age was identified as the most influential predictor of CAD risk within the studied dataset.
CONCLUSIONS: The Gradient Boosting machine learning model demonstrated superior performance in predicting CAD within the Taiwanese cohort, with age being a critical predictor. These findings underscore the potential of machine learning models in enhancing the prediction accuracy of CAD, thereby supporting early detection and targeted intervention strategies.
BACKGROUND: Not applicable.