Identifying Risk Factors for Caregiver Burden in Neurological Disorders Using Machine Learning

Background: Caregiver burden represents a multidimensional syndrome influenced by patient-related, relational, and contextual factors in neurological disorders. Although stroke, Parkinson's disease (PD), and Alzheimer's disease (AD) differ in clinical trajectory, comparative analyses of caregiver risk profiles across these conditions remain limited. Objective: This study aimed to identify sociodemographic, cognitive, and dyadic factors associated with caregiver burden in a clinical cohort, and to investigate their value for caregiver risk stratification using supervised machine learning models. Methods: In this monocentric observational cohort study, 113 patient-caregiver dyads (79 stroke, 19 PD, 15 AD) were consecutively enrolled in a neurorehabilitation setting. Patients underwent cognitive assessment with the Montreal Cognitive Assessment (MoCA), while caregivers completed the Caregiver Burden Inventory (CBI), which was considered the primary outcome measure. Caregiver burden was dichotomized into mild versus moderate-to-severe burden using established CBI cutoff thresholds. Group comparisons, correlation analyses (false discovery rate-corrected), and supervised machine learning models (logistic regression, random forest, AdaBoost, support-vector machine, naïve Bayes, and CatBoost) were performed using 5-fold stratified cross-validation repeated 10 times. Results: Disease-specific burden patterns emerged. Stroke caregivers reported higher time-dependent burden, whereas AD caregivers showed greater emotional burden (p < 0.05). No significant differences were observed in total CBI scores across groups. Lower MoCA scores were moderately associated with higher total and time-dependent burden (r up to -0.49, p < 0.001), particularly when interacting with advanced patient age. Machine learning models showed moderate performance, with AdaBoost achieving the highest accuracy (75.8%) and logistic regression and CatBoost the highest area under the receiver-operating-characteristic curve (AUC = 0.80). The most influential predictors were the age × MoCA interaction, MoCA score alone, and patient-caregiver gender concordance. Cognitive impairment, especially in older patients, and dyadic gender concordance emerged as central risk factors. Conclusions: Multidimensional assessment and early risk stratification, potentially supported by machine learning tools, may improve identification of vulnerable caregivers and guide tailored interventions.