一个可解释的LightGBM模型用于预测冠心病:通过机器学习增强临床决策
Lang Deng1,2, Kongjie Lu1,2, Huanhuan Hu1,2
1Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China.
PloS one
|September 12, 2025
概括
本研究介绍了一种基于LightGBM的框架,用于预测冠心病 (CHD) 风险,提高诊断准确性和可解释性. 关键预测因素包括年龄,吸烟,糖尿病,高血压和高胆固醇.
科学领域:
- 心血管疾病研究研究
- 医疗保健中的机器学习
- 生物医学数据科学 生物医学数据科学
背景情况:
- 冠状动脉心脏病 (CHD) 构成了全球健康的重大负担.
- 传统的CHD诊断方法,如血管造影和心电图,具有包括成本,主观性和误诊在内的局限性.
- 需要准确和可解释的CHD风险预测工具.
研究的目的:
- 开发和验证冠心病 (CHD) 风险的新型预测框架.
- 通过机器学习提高CHD风险评估的准确性和可解释性.
- 为临床风险分层创建一个用户友好的评分系统.
主要方法:
- 使用了三个公共数据集 (BRFSS_2015,Framingham,Z-Alizadeh Sani) 进行模型培训和验证.
- 采用LightGBM算法进行高效和高性能的CHD预测.
- 整合了SHAP (夏普利添加式解释) 值来解释模型的可解释性,并开发了一个CHD评分系统.
主要成果:
- 在BRFSS_2015数据集中,LightGBM模型实现了高性能,精度高达90.61%,AUROC高达81.11%.
- 在Framingham和Z-Alizadeh Sani数据集上的验证显示了更好的准确性 (分别高达85.26%和80.33%) 和AUROC (高达67.37%).
- SHAP分析确定年龄,吸烟状态,糖尿病,高血压和高胆固醇是心脏病风险的关键预测因素.
结论:
- 拟议的LightGBM框架为CHD风险预测提供了一个准确和可解释的方法.
- SHAP分析提供了对驱动心血管疾病风险的关键因素的宝贵见解.
- 开发的CHD评分系统是临床医生评估和管理患者风险的实用工具.
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