Subitem-level multi-scale assessment and machine learning for three-class cognitive status classification in Parkinson's disease.

ABSTRACT: People with Parkinson's disease (PD) frequently develop cognitive impairments, and early accurate classification of cognitive status is critically important for clinical intervention. In this study, we leveraged data from the Parkinson's Progression Markers Initiative (PPMI) to develop a two-stage machine-learning framework that distinguishes among three cognitive states: PD with normal cognition (PD-NC), PD with mild cognitive impairment (PD-MCI), and PD dementia (PDD). Our approach combined SHapley Additive exPlanations (SHAP) for model interpretability with an ensemble of XGBoost and multilayer perceptron (MLP) classifiers, addressing class imbalance via the SMOTE-Tomek method. All model development and validation were conducted with a strict hold-out evaluation, with the test-set entir

SUBMITTER: Chen YC 

PROVIDER: S-EPMC12769750 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature