The Application of Machine Learning Algorithm Applied to 3Hs Risk Assessment

Hypertension, Hyperglycemia and Hyperlipidemia (3Hs) are the significant factors of Cardiovascular Disease. Considering indicators related to obesity containing Body Mass Index (BMI), Waist Circumference (WC), Hip Circumference (HC), Waist-to-hip Ratio (WHR), Waist-to-height Ratio (WHtR) and disease history, disease history of family, dietary and etc. obtained conveniently and noninvasively, this article mainly set up two models to study the application of algorithm applied to 3Hs risk assessment. According to different combinations and gender, we build prediction model respectively to test the performance of them. In this article, 10-fold cross-validation was used to verify the model. In model I (HCRI - Logistic Model), the logistic regression algorithm was used to train the RC of Harvard cancer risk index. In model II (Logistic - Cart Model), taking the advantage of Decision Tree dealt with continuous variables, we set the output of CART as the input of logistic. The results show that, in HCRI - Logistic Model, the differences between male and female were not obvious, the accuracies are both only close to 70%, and the prediction of hyperglycemia is better than other 2Hs. In Logistic - Cart Model, the prediction of adult female is superior than men using indicators related to obesity. Especially about hyperglycemia, for model II, the accuracy is as high as 89.85% raised by 19.28% compared with model I, the specificity is 96.62% and the sensitivity is 84.56%. It provides an important reference for the evaluation of 3Hs to reduce the growth of relative chronic diseases.