Abstract: Objective  To analyze the influencing factors of mild cognitive impairment based on logistic regression and decision tree.  Methods   Patients were selected from three third-class and first-class hospitals in Beijing, who were diagnosed with end-stage renal disease and underwent hemodialysis or peritoneal dialysis. Based on multivariate logistic regression analysis and CHAID algorithm of decision tree, the influencing factors of mild cognitive impairment in patients with end-stage renal disease were analyzed.  Results   A total of 337 patients with end-stage renal disease were included, the incidence of mild cognitive impairment  was 44.2%. The results of multivariate logistic regression analysis showed that compared with patients aged≤44 years, the risk of mild cognitive impairment at≥60 years is higher (OR=5.637, 95% CI: 2.843～11.175, P<0.001). Compared with patients with education for 6 years or less, those with education for 7 to 12 years (OR=0.138, 95% CI: 0.023～0.827, P=0.030) and more than 12 years (OR=0.076, 95% CI: 0.013～0.454, P=0.004) have lower risk of mild cognitive impairment. Patients with diabetes (OR=2.009, 95% CI: 1.147～3.521, P=0.015) and history of cerebrovascular disease (OR=5.039, 95% CI: 2.281～11.129, P＜0.001) are at higher risk of mild cognitive impairment. A total of 6 variables entered the decision tree model, from top to bottom were age, history of cerebrovascular disease, diabetes, time of education, mode of dialysis and creatinine. Both models have high efficiency.  Conclusion   The combination of decision tree model and traditional logistic model can scientifically and fully explore the influencing factors of mild cognitive impairment in patients with end-stage renal disease. The influencing factors are related to the general condition of patients, complicated disease, treatment methods and examination indicators, which should be paid attention to in clinic. 

Key words: End-stage renal disease; , Mild cognitive impairment; , Influencing factors; , Logistic , regre- ssion, Decision tree