A Robust MI-Based Hybrid Diagnostic Model for Early Detection of Heart Diseases
Bibliographic record
Abstract
Heart disease operates as one of the leading dangerous causes of death worldwide thus humans require both precise and speedy medical diagnosis applications. Machine learning (ML) exhibits impressive potential to boost clinical decision-making each year because it effectively duplicates patterns within complex HiMed data. Machine learning demonstrates pattern imitiation through this capability. The main purpose of this research involved the development of a hybrid machine learning system that predicted heart disease. The system utilizes majority voting ensemble method to unite SVM with DT and RF classifiers for prediction purposes. The research utilizes the Cleveland Heart Disease dataset found at UCI Machine Learning Repository to conduct training and testing operations. The preprocessing procedures contain One-hot category encoding together with normalization of data and Recursive Feature Elimination (RFE) feature selection functionality. The suggested hybrid combination model achieves 92.5% accuracy and 91.8% precision while reaching 93.2% recall and 92.5% F1-score making it perform better than single classifiers. The findings match with the conclusion about the hybrid ensemble approach being more resilient with general capabilities and diagnostic accuracy. Such systems prove to be an excellent practical solution for operational medical decision programs used in actual healthcare settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".