Abstract: Objective This study aims to investigate the prevalence of depressive symptoms and their influencing factors in patients undergoing maintenance hemodialysis (MHD). Methods A multicenter design was adopted, and a total of 397 MHD patients were included. Data on sociodemographic characteristics, laboratory test indicators, and scores from the Patient Health Questionnaire-9 (PHQ-9) were collected. Univariate analysis, logistic regression, and Chi-squared Automatic Interaction Detector (CHAID) decision tree analysis were used to determine the influencing factors.  Results The prevalence of depressive symptoms in MHD patients was 21.91%. Logistic regression analysis indicated that insomnia (OR=2.583，95%CI: 1.472～4.533，P＜0.001), diabetes (OR=1.796，95%CI: 1.026～3.144，P = 0.040), and hepatitis B (OR=3.996，95%CI: 1.786～8.942, P＜0.001) were independent influencing factors of depressive symptoms in MHD patients. CHAID decision tree analysis showed that insomnia (χ2=20.046, P＜0.001), diabetes (χ2=5.543, P=0.019), hypoproteinemia (χ2=19.198, P＜0.001), and β2-microglobulin (β2-MG) (χ2 =10.969, P=0.008) were the main factors affecting depressive symptoms.  Conclusion  Insomnia, diabetes, hepatitis B, hypoalbuminemia, and β2-MG are closely associated with depressive symptoms in MHD patients. This study provides a scientific basis for the early identification and intervention of depressive symptoms in MHD patients.

Key words: Machine learning, Hemodialysis, Depression, Risk factors, Logistic regression, CHAID decision tree