Comparison of random forest and support vector machine for prediction of cognitive impairment in Parkinson's disease
Bibliographic record
Abstract
Cognitive impairments are typical in PD and are indicated by mild cognitive impairment (PD-MCI) in the early stages or dementia (PDD) in higher stages. The Montreal Cognitive Assessment (MoCA) is an instrument commonly used for ascertaining cognitive impairments in PD. This research uses the clinical, neuroimaging, and CSF data as a predictor variable and the MoCA score as the target variable representing cognitive impairments. Machine learning approaches through support vector machine (SVM) and random forest (RF) methods were applied for modeling. The mean absolute error (MAE) and the root mean square error (RMSE) are used to compare the predicted performance values of the method's application. The experimental results showed that both SVM and RF performed well in predicting cognitive impairments in PD patients, indicated by the relatively small MAE value at 0.076 and RMSE at 0.542. This research also discovers that SVM is better than RF in predicting cognitive impairments. Meanwhile, RF presents an apparent and explicable outcome, which is beneficial for determining important variables that correspond to cognitive impairments. The five measurements with the highest mean decrease accuracy (%IncMSE) are age of onset, phosphorylated tau, α-synuclein (aSyn), mean putamen, and total tau.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".