Detection of Automated Parkinson’s disease and Multi Level Feature Enhancement using Artificial Intelligence Techniques
Bibliographic record
Abstract
Early diagnosis and ongoing symptom monitoring are vital for improving life quality in Parkinson's Disease (PD). This study presents a new diagnostic system called the Detection of Automated Parkinson’s Disease and Multi Level Feature Enhancement using Artificial Intelligence Techniques (DAPMLA) model, which aims to improve current methods' accuracy and efficiency. The model uses data from sensors and sketches, along with advanced machine learning techniques, to identify early signs of PD. It incorporates L1-Norm Support Vector Machines (SVM) for selecting important features and uses the Relief algorithm to evaluate feature importance. The dataset includes features from both frequency analysis and sketches, and it trains various algorithms such as CatBoost, XGBoost, Random Forests, AdaBoost, and SVM for classifying PD. The DAPMLA model shows impressive performance, achieving 100% accuracy with balanced data and outperforming existing models. It has a high AUC score of 0.96, indicating good sensitivity and specificity. The DAPMLA model also shows zero false positives and negatives, demonstrating its reliability. A frequency analysis feature helps distinguish important signals, aiding in the understanding of voice and movement indicators of PD. DAPMLA is modular and easy to implement in healthcare systems. Its lightweight nature makes it suitable for remote diagnostics and telemedicine, providing effective, cost-efficient, and noninvasive monitoring.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".