The Risk Prediction of Type 2 Diabetes based on XGBoost
This paper applies the XGBoost method to construct a predictive model for the risk of type 2 diabetes which based on the physical examination data.The paper takes the real physical examination records of the same batch of people in a health check-up center from 2010 to 2015 as the data source,and evaluates the feature importance.Finally,28 characteristic variables are selected as the model input,and a phase is obtained.Compared with other common classification algorithms,the prediction model with higher prediction accuracy and stronger generalization ability has certain clinical reference value for the risk prediction of type 2 diabetes.
XGBoost type 2 diabetes risk prediction
Wei Ji Shaofu Lin
Faculty of Information Technology,Beijing University of Technology,Beijing,China
国际会议
西安
英文
145-150
2019-01-19(万方平台首次上网日期,不代表论文的发表时间)