Ensemble Machine Learning Based Identification of Adult Epilepsy
Why this work is in the frame
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Bibliographic record
Abstract
Epilepsy is a chronic non-communicable illness that affects brain individuals and impacts more than 50 million people globally.To predict epileptic seizures, we proposed machine learning-based ensemble learning technique in this study.In the preprocessed stage, we applied some important techniques such as Power line noise reduction and dividing the record into windows of 5 seconds.The project is created by the help of ensemble machine learning technique, which employs several machine learning algorithms, we used the following algorithms: decision tree, support vector machine, artificial neural networks, and convolutional neural networks.We used a dataset from PhysioNet website that contains adult EEG signals.Several convolutional layers were used to extract features from the EEG signals, after that, the feature set is utilized to train a classifier model, which combines the results.Our approach successfully reached 91% accuracy while 91% sensitivity and 91% specificity, respectively.
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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