Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease

• Published in: PloS one Vol. 20; no. 6; p. e0324632
• Main Authors: Cao, Meng, Tang, Bixia, Yang, Liwei, Zeng, Jing
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.06.2025; Public Library of Science (PLoS)
• Subjects: Aged; Algorithms; Biology and Life Sciences; Body mass index; Care and treatment; China - epidemiology; Cholesterol; Chronic kidney failure; Cognition & reasoning; Cognition disorders; Cognitive ability; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - epidemiology; Cognitive Dysfunction - etiology; Complications and side effects; Computer and Information Sciences; Education; Female; Health aspects; Health risks; Hemodialysis; Hemoglobin; Humans; Impairment; Kidney diseases; Kidneys; Learning algorithms; Logistic Models; Longitudinal Studies; Machine Learning; Male; Medicine and Health Sciences; Memory; Mental depression; Methods; Middle Aged; Neural networks; Neural Networks, Computer; Older people; Participation; Patient outcomes; Physical Sciences; Population; Prediction models; Prevention; Prognosis; Quality of life; Questionnaires; Regression analysis; Renal Insufficiency, Chronic - complications; Research and Analysis Methods; Risk; Risk Assessment; Risk Factors; Social Sciences; Support Vector Machine; Support vector machines; Variables; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0324632

The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored. This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention. A total of 415 CKD patients from the 2015 China Health and Retirement Longitudinal Survey (CHARLS) dataset were included in this study. Participants were categorized into two groups: the CI group (n = 53) and the non-CI group (n = 362). Binary logistic regression, encompassing both univariate and multivariate analyses, was conducted to identify influencing factors. Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library. Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. Calibration curves demonstrated that all models were well-calibrated. Among these, the NNET model exhibited the highest predictive performance. According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration. Machine learning models are valuable tools for predicting the risk of CI in CKD patients and can assist healthcare professionals in developing appropriate intervention strategies.