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Prediction of Adult Chronic Kidney Disease with Class-Imbalanced Datasets

In recent years, the incidence of chronic kidney disease (CKD) has been increasing year by year due to unreasonable diet and living habits. CKD is a great threat to human health because of its low awareness rate, high fatality rate and undetectable in the early stage. At present, early detection and early treatment are still the most effective means for the treatment of CKD. However, due to the long collection period, the problem of sparseness and imbalance characteristics of CKD follow-up data makes CKD prediction difficult. Therefore, a resampling method is proposed to preprocess the data and construct a CKD combined classification prediction model based on AUC. The experimental results show that this method can effectively improve the comprehensive ability of model classification. In addition, multivariate regression analysis of the risk factors for new CKD also found that insulin resistance index was also highly associated with new CKD.