Predictive analytics in healthcare epileptic seizure recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction Clinical applications of electroencephalography (EEG) span a very broad range of diagnostic conditions. Epileptic seizure is the fourth most common neurological disorder in that. Related Work There has been considerable progress in clinical understanding of epilepsy, however many aspects of seizure prevention are still a mystery. Predictive modeling of EEG can provide significant value addition to substantiate the diagnosis of epilepsy. Methodology Machine learning algorithms are applied to predict the probability of epileptic seizure using an open source multi-class dataset. Results and Discussion Comparing the F-score from different classifiers, it is found that XGBoost gives the best performance in binary classification and Random Forest provides the best performance in multinomial classification. Conclusion Our results show that it is possible to predict epileptic seizure with significant accuracy from non-epileptic parameters using a suitable machine learning algorithm. We also observe that binary classification methods have higher prediction accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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