Machine Learning Algorithms for Predicting the Impact of Care Burden on the Psychological Well-being of Caregivers for Chronic Kidney Disease Patients.

Abstract

Background: The aim of this study was to apply Machine Learning (ML) algorithms to predict the impact of care burden on the psychological well-being of caregivers of patients with Chronic Kidney Disease (CKD).

Materials and methods: This cross-sectional study employed an ML approach to analyze data from 200 primary family caregivers of CKD patients undergoing hemodialysis. The caregivers were selected through convenience sampling from hospitals affiliated with Mashhad University of Medical Sciences. Caregivers completed the demographic form, the Novak and Guest Pressure Care Questionnaire, and Ryff's Scales of Psychological Well-being. Four ML algorithms: Random Forest (RF), logistic regression, decision tree (DT), and Support Vector Machine (SVM) with Linear, Polynomial, and Sigmoid Kernels, were evaluated using Python and the Scikit-Learn module in the Anaconda environment.

Results: The RF model achieved the highest accuracy score of 0.70, followed by the polynomial SVM model with 0.68. The SVM linear model scored 0.62, logistic regression and DT models both scored 0.58, and the SVM sigmoid model had the lowest accuracy score of 0.54. The RF algorithm also achieved superior levels of the Area Under the Curve (AUC) (0.72) and sensitivity (0.72%). Eight key predictors of psychological well-being were identified: caregiver burden, age, education, economic situation, number of care days, family members, dialysis days, and the amount of assistance offered by family members to the caregiver.

Conclusions: The RF algorithm, a robust ML tool, effectively analyzed datasets to reveal insights into the relationship between caregiver burden and caregiver well-being in CKD patients.