Abstract: Only a subset of the patients with stage 3 Chronic Kidney Disease (CKD) progresses to stage 4. By observing the clinical data, there are significant differences in physiological indicators between progressive and non-progressive patients. Firstly, a sparse logistic regression (SLR) with L1/2 regularization is proposed, and it is used to select the key factors that influence the progression of CKD. Then, the progression prediction model is built by SLR, Support Vector Machine (SVM) and Adaboost Decision Tree (BOOSTDT). In addition, stacking algorithm (STKSSD) is introduced to overcome the shortcomings of unstable generalization performance due to lack of samples. Finally, Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Networks (BLSTM) are used to model the data respectively. The experimental results show that when 11 key features such as phosphorous,serum creatinine,and so on are selected by the SLR, the STKSSD algorithm achieves the best performance and obtains 86.97% recall rate， 92.86% precision rate, and 89.82% F1-score.

Key words: SLR, STKSSD, SVM, BOOSTDT, BLSTM, ANN, chronic kidney disease, progression prediction