A hybrid disease prediction model based on decision tree and extreme learning machine for predicting dialysis of diabetic nephropathy

Dialysis is a cure for end-stage renal disease. Nephropathy is the leading factor that affects whether diabetic patients need dialysis treatment. In this study, a hybrid disease prediction scheme is proposed to identify critical disease risk factors for predicting diabetic patients who would suffer from nephropathy and need dialysis treatment in the future. The proposed model initially used under sampling based on clustering method to reduce the effect of class-imbalance problem of the real datasets collected from National Health Insurance Research Database (NHIRD). The C5.0 decision tree was then utilized to select important risk factors, and an extreme learning machine using the selected factors as predictors was applied to construct an effective disease prediction model to predict the diabetic patients suffering from nephropathy and needing dialysis. Empirical results revealed that the proposed hybrid disease prediction scheme not only provides better classification accuracy than that of the three competing models in terms of classification accuracy but also exhibits the capability of identifying important risk factors that can provide useful information for identifying high-risk groups for the dialysis of diabetic nephropathy. The results of this study provide an effective and appropriate hybrid disease prediction model to predict the dialysis of diabetic nephropathy. © 2020, Chinese Society for Quality. All rights reserved.