Objective To build the early predictive model for chronic kidney disease(CKD)in hypertension and diabetes patients in the community.Methods The CKD patients were recruited from 4 health care centers in 4 urban areas in Kunming.The control group was residents without hypertension and diabetes(n = 1267).The disease group was residents with hypertension and/or diabetes(n = 566).The questionnaire survey,physical examination,laboratory testing,and 5 SNPs gene types in the PVT1 gene.The risk factors,which were filtered with logistics regression,were used to build predictive models.Four machine learning algorithms were built:support vector machine(SVM),random forest(RF),Na?ve Bayes(NB),and artificial neural network(ANN)models.Results Thirteen indicators included in the final diagnostic model:age,disease type,ethnicity,blood urea nitrogen,creatinine,eGFR from MDRD,ACR,eGFR from EPI2009,PAM13 score,sleep quality survey,staying-up late,PVT1 SNP rs11993333 and rs2720659.The accuracy,specificity,Kappa value,AUC of ROC,and PRC of ANN are greater than those of the other 3 models.The sensitivity of RF is the highest among 4 types of machine learning.Conclusions The ANN predictive model has a good ability of efficiency and classification to predict CKD with hypertension and/or diabetes patients in the community.