PDD-ET: Parkinson’s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset
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 a neurological disorder affecting the nerve cells. PD gives rise to various neurological conditions, including gradual reduction in movement speed, tremors, limb stiffness, and alterations in walking patterns. Identifying Parkinson’s disease in its initial phases is crucial to preserving the well-being of those afflicted. However, accurately identifying PD in its early phases is intricate due to the aging population. Therefore, in this paper, we harnessed machine learning-based ensemble methodologies and focused on the premotor stage of PD to create a precise and reliable early-stage PD detection model named PDD-ET. We compiled a tailored, extensive dataset encompassing patient mobility, medication habits, prior medical history, rigidity, gender, and age group. The PDD-ET model amalgamates the outcomes of various ML techniques, resulting in an impressive 97.52% accuracy in early-stage PD detection. Furthermore, the PDD-ET model effectively distinguishes between multiple stages of PD and accurately categorizes the severity levels of patients affected by PD. The evaluation findings demonstrate that the PDD-ET model outperforms the SVR, CNN, Stacked LSTM, LSTM, GRU, Alex Net, [Decision Tree, RF, and SVR], Deep Neural Network, HOG, Quantum ReLU Activator, Improved KNN, Adaptive Boosting, RF, and Deep Learning Model techniques by the approximate margins of 37%, 30%, 20%, 27%, 25%, 18%, 19%, 27%, 25%, 23%, 45%, 40%, 42%, and 16%, respectively.
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.
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 | 0.001 |
| 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