基于机器学习的心脏病预测模型研究

心脏病作为当今社会对人类生活健康威胁最严重的血管疾病之一，不仅严重威胁人类的生命安全，而且高昂的治疗费用还会给家庭和社会带来严重的经济负担。针对目前心脏病预测研究中存在准确性不足及缺乏特征可解释性的问题，通过挖掘影响心脏病的重要特征，实现对心脏病准确预测和影响因素可解释性分析。首先，利用T检验来分析特征之间的显著差异性（P-value），通过P-value值降序排列选出特征进行组合。然后，利用十种机器学习模型和SHAP方法实现对心脏病的预测及其特征可解释性分析。在UCI心脏病数据集上进行验证实验，在七个医学领域广泛使用的评价指标上都到达了1，优于与对比论文实验结果。最后，利用SHAP方法对13个特征进行可解释性分析，通过特征重要性排序可视化结果，挖掘单个特征与心脏病之间的关联，能为医生对心脏病的精准医疗提供决策支持。

Research on heart disease prediction model based on machine learning

Heart disease, as one of the most serious vascular diseases that threaten human life and health in today's society, not only seriously threatens human life safety, but also brings serious economic burden to families and society due to high treatment costs. Aiming at the problems of insufficient accuracy and lack of feature interpretability in the current heart disease prediction research, by mining the important features that affect heart disease, the accurate prediction of heart disease and the interpretability analysis of influencing factors are realized. First, use the T test to analyze the significant difference (P-value) between the features, and select the features to combine by descending the P-value value. Then, the prediction of heart disease and its feature interpretability analysis were implemented using ten machine learning models and SHAP methods. Validation experiments were performed on the UCI heart disease dataset, and it reached 1 on seven evaluation indicators widely used in medical fields, which was better than and compared with the experimental results of the paper. Finally, the SHAP method is used to analyze the interpretability of 13 features, and the results are visualized through feature importance ranking, and the association between a single feature and heart disease can be mined, which can provide decision support for doctors in precision medicine for heart disease.