Coronary Heart Disease Prediction On Small Datasets: A Comparative Analysis
Bibliographic record
Abstract
Coronary Heart Disease (CHD) is one of the major causes of death worldwide. Bangladesh and other developing nations face similar challenges. Most people wait until it is too late to recognise that their cardiac problems are getting worse. For this reason, early detection is essential to reduce the death toll or major health effects from CHD. This paper’s main goal is to use supervised machine learning (ML) techniques to improve the accuracy of CHD prediction for a Bangladeshi population. ML methods including KNN, Random Forest, Decision Tree, Naive Bayes, and Binary Logistic Regression Model are used in our research methodology to predict CHD on two distinct datasets: one from Bangladesh and the other from Canada. Synthetic data for Bangladeshi dataset were generated by using ADASYN which produces accuracy of 88.12% . On the other hand, using SMOTE, the obtained accuracy was around 93.79%. Both accuracies were achieved by applying Random Forest Algorithm. Binary Logistic Regression obtained highest accuracy for the Canadian dataset which is 72.33%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.011 |
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; both teacher heads agree on what is shown here.
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".