Early Detection of Heart Disease Using Advances of Machine Learning for Large-Scale Patient Datasets
Bibliographic record
Abstract
Heart disease is one of the significant causes of death all over the world. The Healthcare industry produces a large amount of data; thus, heart disease prediction is becoming a challenging task in IoT-based healthcare systems. Machine learning plays a vital role in predicting the disease accurately. Many studies have been conducted in this area; however, they do not use large-size datasets to explore the real power of machine learning techniques in predicting heart disease. In this paper, we used four large-scale multi-dimensionality heart disease datasets collected from different sources. We applied various traditional machine learning techniques, namely Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) to predict heart disease. The results are compared to an ensemble model for the diagnosis of the heart disease.
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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 | 0.000 |
| 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) | 0.001 | 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 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".