An Optimized Machine Learning Approach for Predicting Parkinson's Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Parkinson's disease (PD) is an age-related neurodegenerative disorder affecting millions of elderly people world-wide. The early and accurate diagnosis of PD with available treatment might delay neurodegeneration and prevent disabilities. The existing diagnosis method such as brain scan is an expensive process. The use of speech recognition with machine learning technologies for the diagnosis of PD patients could be less expensive. In this work, we have worked with the voice recorded dataset from UCI machine learning repository. Several studies were performed to identify PD patients from the healthy individuals by using voice recorded data with machine learning algorithms. In this paper, we have proposed an optimized approach of data pre-processing that enhances prediction accuracy for diagnosing PD. We obtain 97.4% prediction accuracy with higher sensitivity, specificity, precision, F1 score and kappa value by using AdaBoost. These improved performance evaluation metrics indicate, the use of voice recording with our optimised machine learning approach is highly reliable in prediction of PD. This approach may have significant implications for early stage diagnosis of PD in a cost-effective manner.
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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 it