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September 10, 2025Open Access

Diagnostic Classification of Mild Cognitive Impairment in Parkinson’s Disease Using Subject-Level Stratified Machine-Learning Analysis

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Authors

JWJing WangYCYan-Fang ChenXXXie Xiao

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Overview

Analysis demonstrates effective machine learning models for classifying mild cognitive impairment in Parkinson’s disease, suggesting improved clinical management.

Key Points

  • SVM showed the highest overall performance with an AUC-ROC of 0.7252 in detecting mild cognitive impairment.
  • Feature importance analysis revealed age, education years, and disease duration as key predictors for distinguishing PD-MCI.
  • The study utilized subject-level stratified sampling to prevent data leakage during model training and testing.
  • The findings suggest that data-driven tools can enhance early cognitive impairment screening in Parkinson’s disease care.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68c1c31b54b1d3bfb60f09eahttps://doi.org/10.20944/preprints202508.0655.v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning-based stratification of mild cognitive impairment in Parkinson’s disease: a multicenter cross-sectional analysis2025
  2. 2Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson’s disease2026 · 2 citations
  3. 3Creation of a machine learning-based model for early identification of mild cognitive impairment risk in Parkinson's disease: A PD research center-based population study2026
  4. 4Construction of a mild cognitive impairment prediction model for Parkinson’s disease patients on the basis of multimodal data2025
  5. 5Subitem-level multi-scale assessment and machine learning for three-class cognitive status classification in Parkinson’s disease2025 · 2 citations