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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".