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March 13, 20260 citations

Machine Learning Model for Early Detection of Mild Cognitive Impairment in Parkinson's Disease

Creation of a machine learning-based model for early identification of mild cognitive impairment risk in Parkinson's disease: A PD research center-based population study.

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Authors

ZYZhengting YangSLSufang LiuLWLu Wang

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Overview

Population study develops a predictive model for mild cognitive impairment risk in Parkinson's patients, suggesting early identification approaches.

Key Points

  • The aim is to create a machine learning model for early identification of mild cognitive impairment risk in Parkinson's disease patients.
  • Used assessment scales and blood tests from 523 Parkinson's patients
  • Developed ten machine learning algorithms for PD-MCI risk prediction
  • Determined optimal model performance through external validation with 139 patients
  • AUC for models ranged from 0.57 to 0.72, with the best model (RF) achieving an AUC of 0.72
  • U3 score identified as the most significant predictor of PD-MCI
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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69b3acc502a1e69014ccebe4https://doi.org/10.1177/1877718x261424696
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Also Consider

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

  1. 1Diagnostic Classification of Mild Cognitive Impairment in Parkinson’s Disease Using Subject-Level Stratified Machine-Learning Analysis2025
  2. 2Machine learning-based stratification of mild cognitive impairment in Parkinson’s disease: a multicenter cross-sectional analysis2025
  3. 3Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson’s disease2026 · 2 citations
  4. 4Construction of a mild cognitive impairment prediction model for Parkinson’s disease patients on the basis of multimodal data2025
  5. 5Interpretable machine learning for cognitive impairment prediction in Parkinson’s disease: a multicenter validation study with SHAP analysis2025