Interpretable machine learning for cognitive impairment prediction in Parkinson's disease: a multicenter validation study with SHAP analysis.
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 interpretabl...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 14 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189571066&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189571066 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2025 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 189571066 189571066 189571066 10.3389/fnagi.2025.1688653 189571066 ppf: 1 ppct: 13 formats: tig: atl: Interpretable machine learning for cognitive impairment prediction in Parkinson's disease: a multicenter validation study with SHAP analysis. aug: au: Wang, Ziyuan Yan, Junqiang affil: Key Laboratory of Neuromolecular Biology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China sug: subj: Machine Learning Methods Prediction Models Evaluation Cognition Disorders Risk Factors Parkinson Disease Complications Risk Assessment Conceptual Framework Human Multicenter Studies Validation Studies Male Female Middle Age Aged Neuropsychological Tests Scales Biological Markers Random Forest Evaluation Boosting Machine Learning Algorithms Evaluation Neural Networks (Computer) Evaluation Prediction Algorithms Evaluation Neutrophil Lymphocyte Ratio Uric Acid Blood Inflammation Antioxidants Analysis Hematopoiesis Cognition Evaluation Confidence Intervals Unpaired T-Tests Mann-Whitney U Test Kendall's tau Chi Square Test Pearson's Correlation Coefficient Comparative Studies Electrolytes Blood Hemoglobins Blood Erythrocytes Health Services Needs and Demand Health Services Accessibility ROC Curve Descriptive Statistics Data Analysis Software Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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 features—encompassing hematological parameters, metabolic markers, and demographics—were preprocessed with synthetic minority over-sampling. Four machine learning models were trained and optimized via nested 5-fold cross-validation. Results: The Random Forest algorithm achieved superior performance in the discovery cohort (AUC = 0.83), outperforming CatBoost (AUC = 0.82), XGBoost (AUC = 0.79), and neural networks (AUC = 0.66). External validation of the framework preserved 71.57% accuracy. SHAP interpretability analysis identified age, neutrophil-to-lymphocyte ratio (NLR), and serum uric acid as critical predictors, revealing synergistic risk effects between elevated inflammation markers and reduced antioxidant levels. Discussion: This framework demonstrates diagnostic accuracy comparable to advanced neuroimaging while utilizing readily available clinical data, enhancing accessibility in resource-limited settings. It highlights neuroinflammation and oxidative stress as key mechanistic drivers of PD-CI, advancing pathophysiological understanding. Multicenter validation confirms the model's robustness across ethnic populations, supporting its utility as a clinically actionable tool for PD-CI screening and monitoring. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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