Predicting cognitive impairment in Parkinson’s disease: a machine learning approach based on clinical and neuropsychological data
Bibliographic record
Abstract
Background: Cognitive impairment is a common and disabling non-motor symptom of Parkinson's disease, markedly diminishing quality of life and elevating caregiver burden. Although considerable research has been conducted, the early prediction of cognitive impairment remains challenging owing to heterogeneous clinical presentations, variations in treatment adherence, and the inherent limitations in sensitivity of conventional biomarkers and cognitive assessment tools. Methods and materials: = 155). Demographic, clinical, biochemical, and neuropsychological variables were obtained at baseline. Cognitive impairment was defined based on Mini-Mental State Examination scores falling below education-adjusted thresholds and further validated using the Montreal Cognitive Assessment. Multiple machine learning models-including Random Forest, Logistic Regression, Gradient Boosting, CatBoost, and Support Vector Machine-were developed and evaluated using the area under the receiver operating characteristic curve, accuracy, recall, F1-score, calibration, and decision curve analysis. Feature importance analysis was performed to identify key predictive variables. Results: During follow-up, patients who developed cognitive impairment were significantly older and had longer disease duration, lower levels of albumin, hematocrit, and blood lipids, as well as a higher prevalence of hypertension. Feature selection identified: Age, Platelet count, Time from diagnosis to baseline visit, Apolipoprotein B, and Hematocrit as the predictors. The Random Forest model demonstrated the best overall performance, with the area under the receiver operating characteristic curve = 0.846, accuracy = 0.75, and an F1-score = 0.775, followed by CatBoost and Logistic Regression. Calibration and decision curve analyses confirmed stable probability estimation and superior clinical utility of Random Forest compared with "treat all" or "treat none" strategies. Further use the Montreal Cognitive Assessment score to verify the stability of the model. Conclusion: Machine learning models integrating multimodal clinical and neuropsychological data demonstrate high accuracy in predicting cognitive impairment in Parkinson's disease, with Random Forest emerging as the most reliable approach. This framework provides a practical tool for early risk stratification, potentially enabling timely interventions and individualized management to reduce the burden of cognitive decline in Parkinson's disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".