Data Sheet 1_Eye-movement-based machine learning for identifying Parkinson’s disease dementia in a retrospective cohort: an interpretable model-development study.docx

SHapley Additive exPlanations (SHAP) were used to interpret the final model.</p>Results<p>Patients with PDD showed poorer cognitive performance and abnormalities in antisaccade inhibition, saccade execution, and fixation stability. Seven predictors were retained: age, sex, education level, antisaccade accuracy, mean overlap-saccade completion duration, mean gap-saccade velocity, and lateral-gaze deviation duration >4°.

Logistic regression and elastic net achieved the highest test-set ROC AUCs (both 0.962), while logistic regression had the lowest Brier score (0.077). A radial basis function support vector machine (SVM) was retained as the primary classifier for detailed evaluation because it provided balanced performance across threshold-dependent metrics and could capture potentially nonlinear relationships and interactions. In the test set, the SVM achieved a ROC AUC of 0.947 and PR AUC of 0.979.

Accuracy, sensitivity, specificity, and F1 score were 0.867, 0.870, 0.857, and 0.904, respectively. SHAP identified education, antisaccade accuracy, mean overlap-saccade completion duration, mean gap-saccade velocity, and lateral-gaze deviation duration >4° as the leading contributors.</p>Conclusion<p>A seven-feature model combining demographic and eye-movement measures showed promising internal discrimination of PDD from PD without dementia.

Eye-movement-based machine learning may complement clinical cognitive assessment, but external validation in larger and more representative cohorts is required before clinical use.</p>