Research on Parameter Feature Analysis and Auxiliary Prediction Model of Coronary Heart Disease Array Pulse Graph Based on XGBoost Algorithm
Objective To study the characteristics of array pulse diagram parameters of patients with coronary heart disease(CHD),and explore the establishment of auxiliary prediction model of CHD based on extreme gradient boosting(XGBoost).Methods 106 normal people(normal control group)and 300 patients with CHD(CHD group)were included in the community geriatric physical examination center.The pulse data of the subjects were collected by 24-point array pulse detector.The differences of array pulse pattern parameters between the CHD group and the normal control group were analyzed,and the auxiliary prediction model of CHD was established based on XGBoost algorithm.Results The comparative difference results of the three pulse map channel data analysis methods were similar,and h1,h4,h5,h1/t1 and As in the CHD group were significantly lower than those in the normal control group(P＜0.05),which was a common diagnostic feature of CHD pulse map among the three analysis methods.APVh1,APVh2,APVh3,APVh4 and APVh5 in CHD group were significantly lower than those in normal control group(P＜0.05).The maximum value channel mean method has the best comprehensive performance,including t1,t3,t4,w1,w2,w1/t and w2/t.Conclusion The characteristic parameters of the array pulse map can reflect the cardiovascular function status of patients with CHD to a certain extent,and the APV index can improve the auxiliary prediction performance of the model.
万方数据
维普
