Researches on the illness risk of essential hypertension complicated with coronary heart disease based on machine learning algorithm / 解放军医学杂志

ABSTRACT

[Abstract] 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.