Predicting Heart Attacks in Patients Using Artificial Intelligence Methods
Bibliographic record
Abstract
Today the heart disease is one of the most important causes of death in the world. So its early prediction and diagnosis is important in medical field, which could help in on time treatment, decreasing health costs and decreasing death caused by it. In fact the main goal of using data mining algorithms in medicine by using patients’ data is better utilizing the database and discovering tacit knowledge to help doctors in better decision making. Therefore using data mining and discovering knowledge in cardiovascular centers could create a valuable knowledge, which improves the quality of service provided by managers, and could be used by doctors to predict the future behavior of heart diseases using past records. Also some of the most important applications of data mining and knowledge discovery in heart patients system includes: diagnosing heart attack from various signs and properties, evaluating the risk factors which increases the heart attack. In this article the effort focused on evaluating the previous works on discovering knowledge using data mining in heart diseases field, and also explain the used algorithms in every one of the previous works, to help the future researchers to gain maximum benefits from these abilities. Because of this, in the next sections, first we will explain various works in data mining field using heart patients’ data, and will show the ability of data mining in various applications of heart disease field, and based on a table will show the history of data mining and it’s applications in heart diseases field. Finally we will provide the best methods and algorithms used in various applications of heart diseases using a comparison and will show the results in a table. It is obvious in the diagrams that the suggested method has the best performance and best quality in prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".