The Risk Prediction of Type 2 Diabetes based on XGBoost
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Wei Ji, Shaofu Lin
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