Cardiovascular Disease Prediction: Employing Extra Tree Classifier-Based Feature Selection and Optimized RNN with Artificial Bee Colony
Bibliographic record
Abstract
Cardiovascular disease (CVD) stands as the most widespread severe illness impacting human health on a global scale.Forecasting CVDs in advance becomes more and more crucial as CVDs increase exponentially every day.Deep Learning (DL) algorithms are selfadaptive to recognize patterns and analyze data more effectively in CVD prediction.Over the past few decades, many researchers and practitioners have examined different predictive algorithms, but most of those studies are based on small-sized datasets like less than 10,000 patient records.The major shortcomings of earlier research lie in its reliance on small-sized datasets, elevating the risk of overfitting.In contrast, our study addresses this limitation by utilizing Kaggle's cardiac dataset encompassing 70,000 patients and 11 features.The primary objective of this study is to minimize the risk of overfitting and accurately predict CVD by showcasing the effectiveness of using comprehensive datasets.This paper proposes a hybrid DL methodology by utilizing a Extra Tree Classifier with Artificial Bee Colony optimized Recurrent Neural Network (ETC-ABC-RNN) for accurate classification of CVDs with 96% accuracy.By measuring accuracy, precision, recall, and F1, the efficiency of the system is demonstrated.The outcomes demonstrated that the suggested methodology surpassed various methods in predicting heart disease.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".