Study on the Risk Prediction Models of Overweight and Obesity in Medical Students
10.11783/j.issn.1002-3674.2024.01.006
- VernacularTitle:基于三种预测模型构建医学生超重肥胖风险因素分析
- Author:
Xiaoyu LU
1
;
Yuanli JIA
;
Mengmeng LI
Author Information
1. 华北理工大学公共卫生学院(063000)
- Keywords:
Medical students;
Overweight and obesity;
Logistic regression;
Random forest;
Support vector machine
- From:
Chinese Journal of Health Statistics
2024;41(1):28-34
- CountryChina
- Language:Chinese
-
Abstract:
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.
