基于机器学习算法的原发性高血压并发冠心病的患病风险研究

Researches on the illness risk of essential hypertension complicated with coronary heart disease based on machine learning algorithm

目的筛选原发性高血压并发冠心病发病的危险因素并建立个体风险分类模型,为疾病诊断提供计算机辅助方法。方法收集重庆医科大学医疗大数据平台中2014年1月1日-2019年5月31日确诊的2791例原发性高血压并发冠心病患者及2135例单纯原发性高血压患者的70项临床信息资料,筛选出单因素分析有统计学意义的指标,采用R3.6.1分别构建logistic回归分类模型及BP神经网络、随机森林、极限梯度上升(XGBoost)3种机器学习模型,比较各种模型的相关参数,选择最优的分类模型。结果单因素分析筛选出有统计学意义的指标44项,将其纳入logistic回归分类模型及机器学习模型。Logistic回归分类模型、BP神经网络模型、随机森林模型、XGBoost模型的测试集中分类精度分别为0.852、0.968、0.966、0.976,受试者工作特征曲线下面积(AUC)分别为0.853、0.970、0.967、0.977。将性能最优的XGBoost模型应用于重庆医科大学附属大学城医院心内科临床实践,灵敏度为1.000,特异度为0.912,诊断精度为0.926,AUC为0.956。结论建立的XGBoost模型对原发性高血压并发冠心病有很好的辅助诊断功能,在临床实践中取得了良好的效果。

Objective To study a model of screening the risk factors of essential hypertension complicated with coronary heart disease and establishing the individual risk classification,and provide a computer-aided diagnostic methods for disease occurrence.Methods To collect 70 clinical information including 2791 patients with essential hypertension complicated with coronary heart disease and 2135 patients with simple essential hypertension diagnosed from January 1,2014 to May 31,2019 in Chongqing Medical University medical big data platform,screen out the indicators with statistical differences in single factor analysis.With R3.6.1 to construct logistic regression classification model and 3 machine learning models of BP neural network,random forest and extreme gradient rise(XGBoost),then compare the relevant parameters of various models and select the optimal classification model.Results According to the univariate analysis,44 indexes with statistical difference were selected and included in logistic regression classification model and machine learning model.The classification accuracy in test set of logistic regression classification model,BP neural network model,random forest model,XGBoost model was 0.852,0.968,0.966 and 0.976,respectively,and the area under the work characteristic curve(AUC)of the subjects was 0.853,0.970,0.967 and 0.977,respectively.Applying XGBoost model with optimal performance to clinical practice of cardiology in the University Town Hospital of Chongqing Medical University.The diagnostic sensitivity was 1.000,specificity was 0.912,accuracy was 0.926,and AUC was 0.956.Conclusion Establishment of XGBoost model has a good auxiliary diagnostic function for essential hypertension complicated with coronary heart disease,and has achieved good results in clinical practice.