TY - JOUR AU - Simon, Steven AU - Mandair, Divneet AU - Albakri, Abdel AU - Fohner, Alison AU - Simon, Noah AU - Lange, Leslie AU - Biggs, Mary AU - Mukamal, Kenneth AU - Psaty, Bruce AU - Rosenberg, Michael PY - 2022 DA - 2022/11/2 TI -时间范围对分类精度的影响:机器学习在冠心病预测中的应用JO - JMIR Cardio SP - e38040 VL - 6 IS - 2kw -冠心病KW -风险预测KW -机器学习KW -心脏KW -心脏病KW -临床KW -风险KW -心肌KW -性别AB -背景:许多机器学习方法仅限于结果分类，而不是纵向预测。在临床风险预测中使用机器学习的一种策略是在给定的时间范围内对结果进行分类。然而，如何确定风险预测的最佳时间范围并不为人所知。目的:在本研究中，我们的目的是利用机器学习方法，随着时间范围的增加，确定事件心肌梗死(MI)分类的最佳时间范围。此外，我们试图将这些模型的性能与传统的弗雷明汉心脏研究(FHS)冠心病性别特异性Cox比例风险回归模型进行比较。方法:我们分析了5201名心血管健康研究参与者的单次临床访问数据。我们检查了从基线检查中收集的61个变量，包括人口统计学和生物学数据、病史、药物、血清生物标志物、心电图和超声心动图数据。我们比较了几种机器学习方法(如随机森林、L1回归、梯度增强决策树、支持向量机和k-最近邻)，以预测在500-10,000天的随访时间范围内发生的心肌梗死事件。使用受试者工作特征曲线下面积(AUROC)在20%保留测试集上比较模型。 Variable importance was performed for random forest and L1 regression models across time points. We compared results with the FHS coronary heart disease gender-specific Cox proportional hazards regression functions. Results: There were 4190 participants included in the analysis, with 2522 (60.2%) female participants and an average age of 72.6 years. Over 10,000 days of follow-up, there were 813 incident MI events. The machine learning models were most predictive over moderate follow-up time horizons (ie, 1500-2500 days). Overall, the L1 (Lasso) logistic regression demonstrated the strongest classification accuracy across all time horizons. This model was most predictive at 1500 days follow-up, with an AUROC of 0.71. The most influential variables differed by follow-up time and model, with gender being the most important feature for the L1 regression and weight for the random forest model across all time frames. Compared with the Framingham Cox function, the L1 and random forest models performed better across all time frames beyond 1500 days. Conclusions: In a population free of coronary heart disease, machine learning techniques can be used to predict incident MI at varying time horizons with reasonable accuracy, with the strongest prediction accuracy in moderate follow-up periods. Validation across additional populations is needed to confirm the validity of this approach in risk prediction. SN - 2561-1011 UR - https://cardio.www.mybigtv.com/2022/2/e38040 UR - https://doi.org/10.2196/38040 UR - http://www.ncbi.nlm.nih.gov/pubmed/36322114 DO - 10.2196/38040 ID - info:doi/10.2196/38040 ER -