Interpretable machine learning for cognitive impairment prediction in Parkinson's disease: a multicenter validation study with SHAP analysis.

ABSTRACT:

Introduction

Parkinson's disease (PD)-related cognitive impairment (PD-CI) is a common and impactful complication of PD, yet current predictive models often rely on specialized resources, lack interpretability, or have limited cross-population validation. This study aimed to develop an interpretable machine learning framework for PD-CI detection using only routine clinical data, addressing unmet needs in accessible and generalizable PD care.

Methods

We analyzed 1,279 participants from the Parkinson's Progression Markers Initiative (PPMI) as the discovery cohort and 197 patients from an independent validation cohort. PD-CI was defined by a Montreal Cognitive Assessment (MoCA) score ≤26 and Unified Parkinson's Disease Rating Scale Part I (UPDRS-I) score ≥1. Twenty-one clinical

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12643985 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature