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Handwriting-based Alzheimer’s disease classification on the DARWIN dataset: Repeated nested validation, task-level reduction, and cross-validated explainability

Author

Listed:
  • Tehreem Fatima
  • Tahira Anwar Lashari
  • Momina Moetesum
  • Sana Anwar Lashari

Abstract

Online handwriting provides a low-burden source of cognitive–motor measurements, but small datasets can yield optimistic results when preprocessing, task selection, or explanation is not separated from evaluation. We evaluated eight classifiers on the DARWIN dataset (174 participants; 25 tasks; 450 predictors) using ten repetitions of 5-outer/3-inner-fold nested stratified cross-validation. All preprocessing, hyperparameter selection, and calibration were restricted to outer training data. We added a training-derived operating point, fully nested seven-task selection, cross-model subset transfer, foldwise SHAP analysis, and an all-feature SHAP sensitivity analysis. Random Forest had the highest descriptive ROC-AUC (0.938 ± 0.033 across 50 held-out folds). The Friedman omnibus test indicated model differences (p

Suggested Citation

  • Tehreem Fatima & Tahira Anwar Lashari & Momina Moetesum & Sana Anwar Lashari, 2026. "Handwriting-based Alzheimer’s disease classification on the DARWIN dataset: Repeated nested validation, task-level reduction, and cross-validated explainability," PLOS ONE, Public Library of Science, vol. 21(10), pages 1-16, October.
    • Handle: RePEc:plo:pone00:0361117
    DOI: 10.1371/journal.pone.0361117
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