Unravelling Parkinson’s Disease Prediction: An Evaluation of Feature Selection Techniques with a Focus on PCA and KNN Performance
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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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".