用加权集成模型预测心脏病风险，兼具高准确率与医生可理解的解释。
Interpretable Heart Disease Prediction via a Weighted Ensemble Model: A Large-Scale Study with SHAP and Surrogate Decision Trees
- 融合LightGBM、XGBoost和CNN，通过加权策略提升预测性能。
- 测试AUC达0.8371，筛查时召回率达80.0%，显著优于单一模型。
- 结合SHAP和替代决策树，让预测结果对临床医生透明可信。
心血管疾病（CVD）仍是全球重大健康挑战，亟需可靠且可解释的早期风险预测模型。本研究基于心脏疾病健康指标数据集，构建了一个战略性加权集成模型，整合树模型（LightGBM、XGBoost）与卷积神经网络（CNN），用于预测CVD风险。模型在229,781名患者预处理数据上训练，原始22个特征经工程扩展为25个，通过加权策略缓解类别不平衡问题。最终集成模型在测试集上达到0.8371的AUC（p=0.003），显著优于最优单个模型，且在筛查场景下召回率达80.0%。为增强透明性与临床可解释性，采用替代决策树与SHapley Additive exPlanations（SHAP）进行分析。该模型通过融合多样学习架构并引入可解释性机制，实现高性能与高透明度的平衡，具备在公共卫生筛查中实际部署的潜力。
原文摘要 · Abstract (English)
Cardiovascular disease (CVD) remains a critical global health concern, demanding reliable and interpretable predictive models for early risk assessment. This study presents a large-scale analysis using the Heart Disease Health Indicators Dataset, developing a strategically weighted ensemble model that combines tree-based methods (LightGBM, XGBoost) with a Convolutional Neural Network (CNN) to predict CVD risk. The model was trained on a preprocessed dataset of 229,781 patients where the inherent class imbalance was managed through strategic weighting and feature engineering enhanced the original 22 features to 25. The final ensemble achieves a statistically significant improvement over the best individual model, with a Test AUC of 0.8371 (p=0.003) and is particularly suited for screening with a high recall of 80.0%. To provide transparency and clinical interpretability, surrogate decision trees and SHapley Additive exPlanations (SHAP) are used. The proposed model delivers a combination of robust predictive performance and clinical transparency by blending diverse learning architectures and incorporating explainability through SHAP and surrogate decision trees, making it a strong candidate for real-world deployment in public health screening.
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