Bibliographic record
Abstract
As heart disease is getting more and more attention from people and with the development of machine learning. The author can use machine learning techniques to construct models to analyze patients’ physical and psychological conditions and make prediction about if or not a patient has heart disease or not based on the patient’s personal information it takes. The dataset is a collection of patients’ personal information that have different genders, race, physical condition and psychological condition and so on. Our experiment starts with data preprocessing by trimming off extreme values and incomplete values while balancing the number of data presented by different classes. The author uses four models to predict whether a patient is experiencing heart disease or not which can help patients’ to know their status and alert people not to get heart disease. The author starts with KNN, ANN then support vector machine and eventually Random Forest, the author tune model’s hyperparameters to make sure they are at their best state. At last the author will compare the accuracy with each other to find out which model is efficient and accurate in predicting 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".