Study on the Risk Prediction Models of Overweight and Obesity in Medical Students
Objective To construct logistic regression,random forest and SVM models to predict the influencing factors of overweight and obesity in medical students,and the prediction performance of the three models was compared,so as to obtain the optimal model for the risk assessment of overweight and obesity.Methods Participants included 1 866 medical students from a city in Hebei Province from May to December 2020.The relevant data of overweight and obesity screening were collected through self-test questionnaire;three models of logistic regression,random forest and SVM are constructed by python.Results The test set showed that the accuracy of logistic regression,random forest and SVM models were 96.26%,98.66%and 98.13%respectively;the specificity were 99.77%,100%and 99.00%,respectively;and the AUC were 0.88,0.99 and 0.88 respectively.Random forest is the optimal prediction model;according to the random forest model results,subjective well-being,negative events and students'economic status are more than 10%of weight in the model.Conclusion Subjective well-being,negative events and students'economic status are the main factors affecting the incidence of overweight and obesity in medical students;the prediction performance of random forest model was better than logistic regression model and SVM model.
Medical studentsOverweight and obesityLogistic regressionRandom forestSupport vector machine
万方数据
维普
