Classification of Parkinson’s Disease Using Machine Learning with MoCA Response Dynamics
Bibliographic record
Abstract
Neurodegenerative diseases (NDs), including Parkinson’s and Alzheimer’s disease, pose a significant challenge to global health, and early detection tools are crucial for effective intervention. The adaptation of online screening forms and machine learning methods can lead to better and wider diagnosis, potentially altering the progression of NDs. Therefore, this study examines the diagnostic efficiency of machine learning models using Montreal Cognitive Assessment test results (MoCA) to classify scores of people with Parkinson’s disease (PD) and healthy subjects. For data analysis, we implemented both rule-based modeling using rough set theory (RST) and classic machine learning (ML) techniques such as logistic regression, support vector machines, and random forests. Importantly, the diagnostic accuracy of the best performing model (RST) increased from 80.0% to 93.4% and diagnostic specificity increased from 57.2% to 93.4% when the MoCA score was combined with temporal metrics such as IRT—instrumental reaction time and TTS—submission time. This highlights that online platforms are able to detect subtle signs of bradykinesia (a hallmark symptom of Parkinson’s disease) and use this as a biomarker to provide more precise and specific diagnosis. Despite the constrained number of participants (15 Parkinson’s disease patients and 16 healthy controls), the results suggest that incorporating time-based metrics into cognitive screening algorithms may significantly improve their diagnostic capabilities. Therefore, these findings recommend the inclusion of temporal dynamics in MoCA assessments, which may potentially improve the early detection of NDs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".