Unravelling Parkinson’s Disease Prediction: An Evaluation of Feature Selection Techniques with a Focus on PCA and KNN Performance
Why this work is in the frame
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Bibliographic record
Abstract
Parkinson's disease is a brain condition that causes involuntary or uncontrolled movements, including tremors, rigidity, and problems with balance and coordination.People of various racial and cultural backgrounds are affected by Parkinson's disease.Early diagnosis of Parkinson's disease is essential to slow neurodegeneration, making the disease's prognosis even more important.This paper explores the prediction of Parkinson's disease utilizing various feature selection techniques and combinations of classifiers.Four distinct feature selection techniques: variance threshold, information gain, chi-square, and principal component analysis (PCA) are utilized in this research.We have adopted Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gaussian Naive Bayes, XGBoost, and AdaBoost classification techniques to predict Parkinson's disease.For the experimental evaluation, we have used the UCI machine learning Parkinson's speech recording signal dataset.The combination of PCA and KNN for correlation distance function provides 92.10% accuracy which is superior performance compared to other combinations of feature selection techniques and machine learning classifiers.In the future, if AI-based predictive models of Parkinson's disease can be developed, healthcare professionals will benefit from reducing neurodegeneration.
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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