Subitem-level multi-scale assessment and machine learning for three-class cognitive status classification in Parkinson’s disease
Bibliographic record
Abstract
People with Parkinson's disease (PD) frequently develop cognitive impairments, and early accurate classification of cognitive status is critically important for clinical intervention. In this study, we leveraged data from the Parkinson's Progression Markers Initiative (PPMI) to develop a two-stage machine-learning framework that distinguishes among three cognitive states: PD with normal cognition (PD-NC), PD with mild cognitive impairment (PD-MCI), and PD dementia (PDD). Our approach combined SHapley Additive exPlanations (SHAP) for model interpretability with an ensemble of XGBoost and multilayer perceptron (MLP) classifiers, addressing class imbalance via the SMOTE-Tomek method. All model development and validation were conducted with a strict hold-out evaluation, with the test-set entirely excluded from feature selection, model training, and threshold optimization. Independent validation demonstrated strong and balanced classification performance across all cognitive subgroups, with particularly effective identification of cognitively impaired individuals requiring clinical attention. The area under the receiver operating characteristic curve (AUC) for three-class discrimination exceeded 0.85. Key predictors, including Montreal Cognitive Assessment (MoCA) scores and activities of daily living assessments, were validated as clinically meaningful by SHAP analysis. The proposed two-stage explainable model demonstrates strong and balanced classification performance across cognitive subgroups in PD. Its ability to identify people at high risk for dementia highlights its potential utility in clinical workflows, particularly as a scalable tool for early cognitive stratification and decision support in routine neurology practice. However, external validation on diverse real-world cohorts is warranted before clinical implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".