A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality of life. Early identification of at-risk individuals and timely intervention are essential. Nevertheless, current CF risk prediction models fall short in accuracy to adequately fulfill clinical requirements. This study aimed to examine the determinants of CF in elderly patients undergoing MHD and to develop a risk prediction model through machine learning algorithms. The objective is to furnish healthcare professionals with an early prediction tool and to offer insights for personalized CF risk management. A convenience sampling method was employed to select 1,075 elderly MHD patients from various tertiary-level hospitals in Chengdu between October 2023 and March 2024 as the modeling set, and 269 elderly MHD patients from hospitals in Chengdu, Yibin, and Zigong between September 2024 and October 2024 as the external validation set.CF was assessed using the Fried Phenotypic Scale for Frailty (FP) and the Montreal Cognitive Assessment Scale (MOCA). Data on patients' demographics, sleep, nutrition, depression, and social support were collected. Single-factor and multi-factor logistic regression analyses were conducted to identify the factors influencing CF. Five machine learning algorithms-Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Neural Network (NNET), and Logistic Regression (LR)-were employed to develop risk prediction models. These five models served as base classifiers, and 16 ensemble models were constructed using the Stacking method. The optimal ensemble models were identified and compared with the five individual models, followed by the selection and external validation of the most effective predictive models. Finally, the optimal models were deployed on web platforms utilizing the Streamlit library. CF prevalence was 14.2%. Significant CF risk factors included age, mode of residence, medical payment method, exercise, alcohol consumption, dialysis vascular access, serum albumin classification, serum phosphorus classification, total cholesterol classification, blood urea nitrogen classification, malnutrition score and depression score. The Stacking model showed superior performance (AUC = 0.911), with external validation confirming its accuracy (AUC = 0.832). Machine learning models, particularly Stacking, effectively predict CF risk in elderly MHD patients, providing a valuable tool for clinical intervention.