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

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 regress