Computer Engineering ›› 2020, Vol. 46 ›› Issue (3): 315-320.doi: 10.19678/j.issn.1000-3428.0054109

• Development Research and Engineering Application • Previous Articles    

Diabetes Risk Prediction Based on GA_Xgboost Model

ZHANG Chunfua,c, WANG Songb, WU Yadongb, WANG Yonga,c, ZHANG Hongyinga,c   

  1. a. School of Information Engineering;b. School of Computer Science and Technology;c. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China
  • Received:2019-03-05 Revised:2019-04-08 Published:2019-05-28

Abstract: Diabetes is a metabolic chronic disease that cannot be thoroughly cured.Early detection and early treatment can reduce the risk of this disease.Machine learning model can effectively predict the disease and provide auxiliary diagnosis and treatment.Therefore,this paper proposes a GA_Xgboost model to predict diabetes risk.Based on Xgboost algorithm,this method makes use of the good global search ability of Genetic Algorithm(GA) to make up for the shortcoming of slow convergence of Xgboost.The elite selection strategy is used to guarantee the best evolutionary results in each round.Experimental results show that the mean square error of GA_Xgboost in diabetes prediction is 0.606,so the prediction accuracy is better than those of the linear regression,decision tree,support vector machine and neural network.Besides,the time of parameter adjustment is 152 s,which is less than grid search and random walk method.

Key words: diabetes prediction, machine learning, auxiliary diagnosis and treatment, GA_Xgboost model, Genetic Algorithm(GA)

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