Organ Risk Prediction for Parkinson’s Disease using Deep Learning Techniques
Bibliographic record
Abstract
Parkinson's Disease is a kind of nervous system disorder whose symptoms start gradually. The signs and symptoms can be different for everyone with no specific diagnosis. Neurological disorders are identified as global threat with Parkinson's Disease being the second most common. More than 10 billion people are living with Parkinson's around the world. This disease is most common in countries of the US and Canada. Deep learning an essential part of Artificial Intelligence provides an uncanny power to systems to construct a complex network using layers of perceptrons that mimic the human neurons. This network Combined with algorithms of Machine Learning and Prognostic Modeling may serve as one of the most powerful tools in healthcare to classify and analyze huge amount of medical data and predict future trends through Supervised Learning. In the paper, we focused on the effective prediction of the organ at risk for Parkinson's Disease (Multi-label Classification). We have examined and refined our model over data collected across data collection of over 300 features. We have put forward an Artificial Neural Network organ risk prediction algorithm using contrasting data. To our finest understanding, none of the previous works have centered on contrasting data in the area of analysis of medical data. The prediction accuracy of our suggested ANN algorithm is 76%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".