Heart Disease Prediction: A Machine Learning Model for Evaluation and Hyperparameter Tuning
Bibliographic record
Abstract
World Health Organization reported that heart diseases are the prominent cause of casualty and also increase year by year. Timely treatment increases the possibility of cure. For this, earlier prediction and accurate diagnosis are essential. Because of today’s technological advancements, prediction with more accuracy and precision is possible. Machine learning (ML) algorithms attract the attention of researchers in prediction modelling due to the accuracy, precision, and reliability of prediction. Hence, in this research an attempt is made to predict the heart diseases using ML algorithms for instance, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). For the research, the heart diseases with 11 parameters and 4 different types of heart diseases especially (TA: Typical Angina; ATA: Atypical Angina; NAP: Non-Anginal Pain, ASY: Asymptonic) are considered and the prediction is done by using the aforementioned Machine Learning algorithms. Finally, the results are compared, and concluded that RF and SVM produce better prediction results.
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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 | 0.015 | 0.011 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".