Enhanced Cardiovascular Disease Classification: Optimizing LSTM-Based Model with Ant-Lion Algorithm for Improved Accuracy
Bibliographic record
Abstract
Cardiovascular diseases pose a significant global health challenge, emphasizing the need for improved techniques in early detection and diagnosis.This study focuses on enhancing the classification of cardiovascular diseases by optimizing an LSTM-based model using the Ant-lion algorithm.In order to achieve this, we utilize the Local Binary Patterns (LBP) technique for feature extraction, which captures important patterns in electrocardiography (ECG) data.The Ant-lion algorithm is then employed to optimize the LSTM model and improve its performance.To evaluate the proposed methodology, we selected the widely used MIT-BIH Arrhythmia Dataset.This dataset contains a variety of heart disease cases, enabling comprehensive testing and validation.In addition to accuracy, we assess various quantitative metrics such as precision, recall, and F1-score to provide a more comprehensive evaluation of the model's performance.This research contributes to the field of ECG classification by highlighting the potential of combining deep learning models with meta-heuristic algorithms.The findings validate the effectiveness of our approach on the MIT-BIH Arrhythmia Dataset, reinforcing the importance of further exploring such optimization techniques in cardiovascular disease diagnosis and management.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".