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

• Published in: Scientific reports Vol. 15; no. 1; pp. 2525 - 14
• Main Authors: Cao, Meng, Tang, Bixia, Yang, Liwei, Zeng, Jing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/4022; 692/499; 692/617; 692/699; Aged; Aged, 80 and over; Algorithms; Cholesterol; Classification; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - epidemiology; Cognitive Dysfunction - etiology; Cognitive frailty; Dialysis; Elderly patients; Female; Frailty; Frailty - etiology; Hemodialysis; Hospitals; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Maintenance hemodialysis; Male; Malnutrition; Middle Aged; multidisciplinary; Neural networks; Nutrition; Patients; Prediction models; Quality of Life; Regression analysis; Renal Dialysis - adverse effects; Risk Assessment; Risk Factors; Risk management; Risk prediction models; Science; Science (multidisciplinary); Social interactions; Support Vector Machine; Cognitive frailty; Risk prediction models; Maintenance hemodialysis; Elderly patients; Machine learning

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...