关键词: 糖尿病, 患病率, 随机森林, 预测

Abstract: Background　China has 114 million people with diabetes，becoming the country with the largest number of diabetic patients all over the world in 2017.Early identification and effective intervention on high-risk population of diabetes can reduce the risk of diabetes mellitus.Objective　To explore the application of random forest algorithm in predicting diabetes mellitus risk in people underwent physical examination.Methods　We used the national health examination data of people at the age of 35 to 74 years who had physical examination at community health service centers in Shiyouxincun and Kaziwan，Urumqi from September 2016 to March 2017.Considering of integrity of the data，the data of 6 727 people underwent physical examination were collected （data from questionnaires，physical measurements and laboratory tests）．The contents of the questionnaire included general demographic data，physical measurements involved height，body mass，and waist circumference and laboratory tests were blood，blood glucose，and serum chemistry indicators.Dataset was divided into a training set and a test set by a ratio of 3∶1.Multivariate Logistic regression analysis and random forest algorithm was used to establish diabetes risk prediction models in the training set and model validation was done with the test set.The prediction efficiency of the model was evaluated by the predicting consistency rate and area under the receiver operating characteristic （ROC） curve.Results　There were 717 cases with diabetes or newly diagnosed with diabetes in 6 727 participants and the prevalence of diabetes mellitus was 10.7%.Among the diabetic patients，the proportion of cases at 65 years and above was 37.1%（266/717）；women were 51.0%（366/717）；Han Chinese was 94.0%（674/717）；people with education level of junior high school was 35.3%（253/717）；overweight was 48.0%（344/717）；non-smokers was 72.8%（522/717）；nondrinkers was 77.0%（552/717）．Multivariate Logistic regression analysis was used to predict the test set of the diabetes risk prediction model established in the training set.The sensitivity was 0.202，the specificity was 0.950，and the prediction consistency rate was 0.696；the Yoden index was 0.151，and the area under the ROC curve （AUC） was 0.685.Random forest algorithm was applied to predict the test set of the diabetes risk prediction model established in the training set.The sensitivity was 0.608，the specificity was 0.953，the prediction rate was 0.864，the Yoden index was 0.561，and the AUC was 0.702.Conclusion　Random forest algorithm has a higher predictive effect on the risk of diabetes for people had physical examination，but multivariate Logistic regression analysis has an intuitive explanation for the influencing factors of diabetes mellitus.We recommend to combine the advantages of the two models in practical applications to maximize their value in disease risk prediction.

Key words: Diabetes mellitus, Prevalence, Random forest, Forecasting