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可解释的机器学习模型用于心血管疾病风险预测:基于特征分解的研究

Liliang Yu¹, Jiancheng Wu¹, Xin Wu²

¹Chongqing Three Gorges Medical College, Chongqing, China.

BMC public health

|October 29, 2025

概括

机器学习准确预测心血管疾病 (CVD) 的风险. 一个新的深度学习模型确定了血压和胆固醇等关键风险因素,有助于早期干预.

科学领域:

计算生物学是一种计算生物学.
医疗信息学医学信息学
机器学习在医疗保健中的应用

背景情况:

心血管疾病 (CVD) 是一个重大的全球健康挑战.
早期预测和鉴定心血管疾病风险因素对于预防至关重要.
机器学习 (ML) 为开发预测模型提供了有前途的工具.

研究的目的:

建立和验证用于预测心血管疾病 (CVD) 风险的机器学习模型.
为了评估基于分解的新型特征深度学习 (FDDL) 模型的性能.
使用模型解释性技术识别CVD的关键预测因素.

主要方法:

使用了来自Kaggle的68,205名受访者的大型数据集.
开发并测试了一个基于特征分解的深度学习 (FDDL) 模型.
将FDDL与其他六个ML模型进行比较,并使用SHAP进行解释.

主要成果:

FDDL模型实现了高预测性能:75.52%的准确性,78.14%的精度,71.68%的回忆,F1得分为0.7522,AUC-ROC为0.7643.
透气血压,胆固醇,静脉血压和年龄被确定为关键预测因素.
后勤回归 (LR) 模型表现最差.

结论:

开发了一种有效的ML模型来预测心血管疾病风险.
该模型可以帮助临床医生识别高风险个体.
为心血管疾病提供个性化的预防性医疗保健策略提供基础.

关键词:

注意力机制注意力机制心血管疾病是什么心血管疾病分解模型的分解模型.机器学习是机器学习.

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