Analysis of Vertical Ground Reaction Force Data in Predicting Parkinson’s Disease
Bibliographic record
Abstract
Introduction: Parkinson’s disease is a complex, progressive neurodegenerative disorder known to negatively impair patient gait. Therefore, with gait and vertical ground reaction force data, an association can be made between the data and Parkinson’s disease. Methods: Data from 146 participants; 93 with Parkinson’s disease and 73 without Parkinson’s disease was obtained from a PhysioNet database for use in this article. A Fourier Analysis and several support vector machine learning models were computed in MATLAB to classify whether an individual had Parkinson’s disease. Results: From the Fourier analysis, it was determined that a statistically significant difference was present between the vertical ground reaction force data of individuals with and without Parkinson’s disease. Additionally, it was found that a Minimum Classification Error Optimized SVM machine learning model using Bayesian statistics was able to classify individuals with Parkinson’s disease using vertical ground reaction force data at an accuracy of 67.1%, and sensitivity of 80.43%. Conclusion: Therefore, it can be determined that vertical ground reaction force can predict Parkinson’s Disease with considerable accuracy which could be improved with an increased number of participants.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".