Developed a Hybrid Bipolar Sigmoid-Recurrent Neural Network with Karush-Kuhn-Tucker- Arithmetic Optimization Algorithm to Predict the Heart Disease
Bibliographic record
Abstract
Heart disease, a leading cause of mortality globally, is increasingly impacting populations worldwide.Effective prediction methods are essential to mitigate this growing health crisis.This study proposes a novel prediction framework, employing a Bipolar Sigmoid-Recurrent Neural Network (BS-RNN), to efficiently classify the heart disease database.Initially, patient health data and body function details are collected and balanced using Apache Kafka before being stored in a cloud database.This balanced data is then pre-processed and subjected to risk analysis.Subsequently, risk and non-risk factors are clustered using the Gibbs Entropy-K-Means Algorithm (GE-KMA), from which features are extracted.The correlation between the extracted features and those trained on the UCI database is assessed using a Pre-Policy Medical Check-Up (PPMC).Subsequently, the Karush-Kuhn-Tucker-Arithmetic Optimization Algorithm (KKT-AOA) is employed to select the optimal correlated features.These features are then input into the BS-RNN classifier for heart disease prediction.In addition to prediction, the framework measures disease severity based on the extracted features.The performance of the proposed model was found to surpass existing techniques, achieving an accuracy of 98.95%, an F-measure of 96.01%, and a specificity of 96.93%.The proposed clustering algorithm also demonstrated efficiency, forming clusters in 6208 ms with a superior prediction rate.These results underscore the potential of the proposed disease prediction framework to outperform existing methods in heart disease prediction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".