基于GA_Xgboost模型的糖尿病风险预测  

Diabetes Risk Prediction Based on GA_Xgboost Model

作　　者：张春富 王松[2] 吴亚东[2] 王勇 张红英[1,3] ZHANG Chunfu;WANG Song;WU Yadong;WANG Yong;ZHANG Hongying(School of Information Engineering,Southwest University of Science and Technology,Mianyang,Sichuan 621010,China;School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang,Sichuan 621010,China;Robot Technology Used for Special Environment Key Laboratory of Sichuan Province,Southwest University of Science and Technology,Mianyang,Sichuan 621010,China)

机构地区：[1]西南科技大学信息工程学院,四川绵阳621010 [2]西南科技大学计算机科学与技术学院,四川绵阳621010 [3]西南科技大学特殊环境机器人技术四川省重点实验室,四川绵阳621010

出　　处：《计算机工程》2020年第3期315-320,共6页Computer Engineering

基　　金：国家自然科学基金(61802320);四川省军民融合研究院开放基金(18sxb030);四川省科技创新苗子工程项目(2018034)。

摘　　要：糖尿病是一种无法根治的代谢性慢性病,早发现、早治疗能降低其发病风险。机器学习模型可以对疾病进行有效预测,提供辅助诊疗。为此,提出一种GA_Xgboost模型应用于糖尿病风险预测。以Xgboost算法为基础,利用遗传算法良好的全局搜索能力弥补Xgboost收敛较慢的缺陷,通过精英选择策略保证每一轮的进化结果最佳。实验结果表明,GA_Xgboost模型在糖尿病预测中的均方误差为0.606,预测精度优于线性回归、决策树、支持向量机和神经网络等算法,调参时间为152 s,用时少于网格搜索和随机游走方法。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.

关 键 词：糖尿病预测 机器学习 辅助诊疗 GA_Xgboost模型 遗传算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]