Predicting the Chance of Heart Attack with a Machine Learning Approach – Supervised Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine learning is a multidisciplinary field combining statistics, computer science and artificial intelligence. This research finds a way to use machine learning to predict the chance of heart attack based on information about the patient. There are 13 features collected about each patient which are age, sex, cholesterol, chest pain type, maximum heart rate achieved, resting blood pressure, resting electrocardiographic results, fasting blood sugar, exercise-induced angina, previous peak, slope, number of major blood vessels, and thalassemia. The information of all the patients is put into a dataset. The dataset is split into two sets, one for training and another for validation. A computer model using a supervised learning algorithm is developed and trained to predict the chance of heart attack. During training, the training set is used for training the model, while the validation set is used for evaluating the accuracy of the model.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.009 |
| 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 it