Machine learning-based stratification of mild cognitive impairment in Parkinson’s disease: a multicenter cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is a prominent non-motor manifestation of Parkinson's disease (PD) and is associated with reduced quality of life, increased mortality, and higher healthcare utilization. We aimed to develop and externally validate a machine-learning model, trained on Montreal Cognitive Assessment (MoCA)-based Movement Disorder Society (MDS) Level I labels, that estimates the contemporaneous probability of mild cognitive impairment in PD (PD-MCI) from routinely collected clinical variables, enabling clinicians to prioritize MoCA-normal patients with higher model-estimated probability for MDS Level II neuropsychological evaluation and closer follow-up. METHODS: We analyzed 799 participants with PD from the Parkinson's Progression Markers Initiative (PPMI), randomly assigning them to training (n = 559) and internal validation (n = 240) cohorts. An independent external cohort comprised 70 consecutive patients recruited at The Affiliated Hospital of Guilin Medical University between February 2024 and March 2025. The reference outcome was MoCA-based PD-MCI (21-25) versus cognitively normal PD (26-30). Candidate predictors were screened by LASSO (1-SE criterion). To handle class imbalance, SMOTE was applied only during model fitting; both validation cohorts retained native class distributions. Five machine-learning models (logistic regression [LR], support vector machine, XGBoost, neural network, LightGBM) were evaluated on non-resampled data for discrimination (area under the receiver operating characteristic curve, AUC), calibration, and clinical utility (decision-curve analysis, DCA). Interpretability combined a nomogram with Shapley additive explanations (SHAP); a bilingual web calculator was also implemented. RESULTS: Of 799 PPMI participants, 169 (21.2%) met the MoCA-based PD-MCI definition. Seven routinely collected predictors were retained (sex, age, education, age at disease onset, MDS-UPDRS Part III, GDS, UPSIT). LR showed the most balanced performance: AUC 0.789 (training), 0.778 (internal), and 0.772 (external). At a fixed threshold of 0.50 in the external cohort, LR's sensitivity was 89.7%, specificity 43.9%, and F1-score 66.7%. Calibration and DCA favored LR. SHAP indicated education and motor severity as dominant contributors, followed by sex and age at onset; depressive burden (GDS) and hyposmia (UPSIT) increased risk, whereas chronological age had a smaller marginal effect. CONCLUSIONS: We developed and externally validated a probability-based, clinic-ready risk-stratification tool for PD-MCI using routinely available variables and MoCA-based MDS Level I labels. Implemented as a nomogram and bilingual calculator, it supports sensitivity-oriented triage-especially among MoCA-normal patients-by prioritizing timely MDS Level II evaluation and closer follow-up. The tool complements, rather than replaces, formal diagnostic assessment and does not predict long-term conversion. CLINICAL TRIAL NUMBER: Not applicable. The PPMI study is registered with ClinicalTrials.gov (NCT01141023) and the registration date is June 8, 2010.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".