An Enhanced Chaos-Based Firefly Model for Parkinson's Disease Diagnosis and Classification
Bibliographic record
Abstract
Many researchers are currently attracted to develop predictive models like telediagnosis and telemonitoring by analyzing speech pattern for diagnosing Parkinson's disease (PD). For this purpose, a novel predictive model is proposed in this study by combining an enhanced chaos-based firefly algorithm (ECFA) and support vector machine (SVM) learning with RBF kernel, termed as ECFA-SVM, for Parkinson's disease diagnosis. The ECFA model will identify relevant combination of features that will help SVM in building an effective predictive model for PD dataset. In this study, selecting genetic algorithm (GA) enhances the functional characteristic of CFA by generating diversified initial candidate fireflies of the population which is then used by the CFA to update the position of candidates for the next iteration, thereby produces optimal subset of features based on SVM. The effectiveness of the model is then assessed on a set of performance metrics such as size of the feature subset, classification accuracy, sensitivity, precision, specificity, ROC area, and F-measure applying on PD dataset obtained from UCI repository. The efficiency of the model is established comparing against GA-SVM and CFA-SVM models on the same PD dataset. The comparison of the performance of the models was studied from four important perspectives such as the number of features, classification accuracy, area under ROC curve and error matrix to make sure its superiority of feature selection and classification over the counterparts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".