Redefined Classification of Hepatitis Variants through Arima, Signal Processing and Machine Learning
Bibliographic record
Abstract
This study introduces a novel feature selection method using the Auto Regressive Moving Average model to classify four hepatitis virus types: Hepatitis B, C, D, and E. Firstly, DNA sequences are transformed from characters into numerical representations using Electron-Ion Interaction Potential coding.Then discrete sine transform is applied to extract features from the sequences.Our proposed feature selection technique, using the inverse integrated moving average (ARIMA) as a statistical filter, refines the features for optimal classification.We train two machine learning models Light Gradient Boosting Machine and Random Forest on the selected features.We compare the results of our approach with another 5 window-based finite impulse response filters (FIR) as they extract significant features by reducing noise and emphasizing key patterns.Our findings indicate that ARIMA, with an order of (1,1,5), achieves 98% accuracy, while the Bartlett window-based filter yields 93.25% accuracy.This approach highlights the potential of ARIMA for effective feature selection in the classification of hepatitis viruses using machine learning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".