WEN-CHOU CHI2026-10-162026-10-162026-01-01Pure ID: 15247777Pure UUID: e71f9944-ee6e-47db-8d6c-9e3e4b67763610.1016/j.jfma.2026.04.085https://www.scopus.com/pages/publications/105037739920https://scholars.csmu.edu.tw/handle/123456789/31756Background: Disability assessment in dementia is important for care planning, but the full World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is time-intensive and may limit clinical use. This study developed machine learning (ML)-based short forms of the WHODAS 2.0 and examined their reliability, concurrent validity, and responsiveness. Methods: Using data from 51,245 persons with dementia (training set: n = 31,952; validation set: n = 19,293), we developed two ML-based short forms, ML-WHODAS-16 and ML-WHODAS-10, with Extreme Gradient Boosting and bootstrap-based item selection under a lock-down training/validation workflow. Their performance was compared with the full WHODAS-32 and the conventional 12-item short form adapted to 11 items after excluding work-related items (Standard-12). Anchor-based longitudinal validity was also examined using deterioration in official Disability Severity Grade. Results: Both ML short forms showed high internal consistency (α = 0.96 for ML-WHODAS-16 and 0.93 for ML-WHODAS-10) and excellent concurrent validity with the full WHODAS-32 (r = 0.98 for both). Compared with the Standard-12, they showed lower error, negligible Bland–Altman bias, and met predefined equivalence criteria, including ±0.5 points. Anchor-based Responsiveness was broadly comparable to the Disability Severity Grade (anchor) (r = 0.66–0.67; standardized response mean = 0.37–0.40). Anchor-based minimal clinically important differences were 9.26 for ML-WHODAS-16 and 9.95 for ML-WHODAS-10. Conclusion: The ML-WHODAS-16 and ML-WHODAS-10 substantially reduced assessment burden while maintaining scores that closely reflected those of the full WHODAS-32, particularly for group-level assessment and longitudinal monitoring. These findings support their use as practical, low-burden alternatives in dementia disability assessment. However, external validation, validation against harder clinical outcomes, formal non-inferiority testing, and clinically anchored longitudinal thresholds remain needed before individual-level interchangeability can be inferred.enjournal article