Diabetes Risk Prediction Based on GA_Xgboost Model
摘要
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.