Heart Disease Prediction using an Ensemble Learning Method: A Study at King Abdullah Hospital in Bisha, Saudi Arabia
Bibliographic record
Abstract
The detection of diseases is essential to improving healthcare outcomes and saving lives. Thanks to technological advancements in medicine, machine learning has become a valuable tool for predicting future patient health outcomes. Despite the abundance of available patient data, accurately predicting cardiac disease has become increasingly challenging. In response, we developed an innovative ensemble learning approach (ELA) that combines three powerful machine learning (ML) techniques. Our ELA provides reliable predictions of cardiac disease that surpass those of the individual classification algorithms, resulting in higher accuracy. Our research yields a new combination of classification algorithms that significantly increases the prediction accuracy. We tested our model on a regional dataset collected from King Abdullah Hospital in Bisha, Saudi Arabia. We obtained the best results false negatives (FN ) of 8, true positives (TP) of 70, true negatives (TN) of 72, false positives (FP) of 6, accuracy of 0.9113, sensitivity of 0.8839, specificity of 0.95, PPV of 0.9389, NPV of 0.8878, AUC of 0.9569, F1 of 0.9133 Kappa of 0.8220, MCC of 0.8277 with an ELA comprising logistic regression (LR), extra trees (ET) and support vector machine (SVM) with radial basis function (RBF) kernel. With our ELA, medical professionals can detect cardiac disease and provide timely interventions to prevent potentially life-threatening health issues.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".