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

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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 p Conclusions: Multidimensional assessment and early risk stratification, potentially supported by machine learning tools, may improve identification of vulnerable caregivers and guide tailored interventions.

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