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

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 < 0.001), but no one of 28 paired comparisons remained significant after Holm correction. Training-derived thresholds (median 0.461) yielded held-out sensitivity 0.869 and specificity 0.831; these are exploratory operating characteristics, not a clinical cutoff. Nested seven-task selection achieved AUC 0.916 ± 0.037, compared with 0.956 ± 0.030 for all 25 tasks, and selected subsets were unstable (mean Jaccard 0.195). Cross-validated SHAP emphasized temporal variables, although an all-450-feature sensitivity analysis showed that individual ranks depend partly on ANOVA preselection. The study contributes an integrated, leakage-controlled evaluation rather than a new classifier or a validated diagnostic instrument; external prospective validation remains necessary.