基于RF-RFECV和LightGBM算法的糖尿病预测  

Prediction of Diabetes Mellitus Using LightGBM Classifier with RF-RFECV

作　　者：刘静乐 罗翔 宫成荣 张国鹏 LIU Jing-le;LUO Xiang;GONG Cheng-rong;ZHANG Guo-peng(Basic Medical Science Academy,Air Force Military Medical University,Xi’an 710032,China)

机构地区：[1]空军军医大学基础医学院,陕西西安710032

出　　处：《计算机与现代化》2023年第11期36-43,50,共9页Computer and Modernization

摘　　要：为了及早发现中国患糖尿病的高危人群并提供有针对性的干预措施,选取代表中国人群的中国健康与养老追踪调查(CHARLS)数据集作为研究对象,提出基于随机森林-交叉验证递归特征消除法(RF-RFECV)和LightGBM的混合算法(RF-RFECV-LightGBM),并与其他5种算法进行实验对比。结果表明RF-RFECV-LightGBM整体性能最优,准确率、精度、召回率、F1值、AUC值分别为0.9772、0.9952、0.8178、0.8978、0.9357。预测时间为0.0428 s,较特征选择前LightGBM的预测时间缩短0.0549 s(提升56.19%),表明了RF-RFECV算法特征选择的有效性。最后,同样的预测流程在皮马印地安人数据集上进行实验,结果达到0.9415的准确率,进一步验证了所提算法的优异性能,可以辅助临床糖尿病诊疗。In order to find the high-risk population of diabetes in China as early as possible and provide targeted intervention measures,the data set of China Health and Retirement Longitudinal Study(CHARLS),which represents the Chinese population,was selected as the research object,and a hybrid algorithm based on RF-RFECV and LightGBM(RF-RFECV-LightGBM)was proposed,and compared with five other algorithms through experiments.The results show that RF-RFECV-LightGBM has the best overall performance,the accuracy,precision,recall,F1 value and AUC value are 0.9772,0.9952,0.8178,0.8978,and 0.9357,respectively.The prediction time is 0.0428 s,which is 0.0549 s shorter than the prediction time of LightGBM before feature selection(increased by 56.19%),indicating the effectiveness of RF-RFECV algorithm.Finally,the same prediction process was tested on the Pima Indian dataset,and the results achieved an accuracy of 0.9415,further verifying the excellent performance of the proposed algorithm RF-RFECV-LightGBM,which can assist in clinical diagnosis and treatment of diabetes.

关 键 词：轻量级梯度提升树 随机森林-交叉验证递归特征消除算法 糖尿病预测 CHARLS数据集 Pima数据集 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]