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构建和验证机器学习模型,以基于非侵入性指标预测冠心病风险

Bo Wu¹, Kang Huang¹, Xin Hong¹

¹Department of Cardiovascular Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.

Computer methods and programs in biomedicine

|December 7, 2025

概括

一个新的机器学习模型使用临床数据准确预测冠心病 (CHD) 风险. 这种工具有助于早期查和预防心血管疾病,这是导致死亡的主要原因.

科学领域:

心脏病学心脏病学
机器学习机器学习
公共卫生公共卫生

背景情况:

冠心病 (CHD) 是全球主要的死亡原因.
早期发现和干预对于管理心血管疾病至关重要.
本研究的重点是开发用于CHD风险预测的机器学习 (ML) 模型.

研究的目的:

开发和评估冠心病 (CHD) 风险的预测模型.
确定导致心脏病风险的关键临床特征.
创建一个用户友好的工具,用于早期的CHD查.

主要方法:

利用行为风险因素监测系统 (BRFSS) 数据集用于模型开发和内部验证.
采用了八个机器学习算法,包括光梯度增强机 (LightGBM).
在外部验证了最佳模型,使用国家健康和营养检查调查 (NHANES) 数据集和SHAP分析来确定特征的重要性.

主要成果:

轻GBM模型在内部验证中实现了0.825的AUC,在外部验证中达到0.851.
确定的主要预测因素包括年龄,性别,高血压和脂质失调.
基于LightGBM模型开发了一个基于Web的计算器,用于基于LightGBM模型的CHD风险预测.

结论:

基于LightGBM的CHD风险预测模型表现出高准确度.
这个模型显示了早期查和预防冠心病的巨大潜力.
开发的工具可以帮助医疗保健专业人员识别高风险个体.

关键词:

这是一个BRFSS数据库.冠心病是一种冠心病.机器学习是机器学习.非侵入性指标非侵入性指标风险预测风险预测

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