A STUDY ON ASSOCIATION RULE MINING FOR VARIOUS HEART DISEASES MEDICAL DATA
Bibliographic record
Abstract
This paper describes our experience on discovering association rules in medical data to predict heart disease. Heart disease is the leading causes of mortality accounting for 32% of all death, a rate is high as in Canada (35%) and USA. Association rule mining a computational intelligence approach is used to identify the factors that contribute to heart disease and Uci Cleveland data set, a biological data base is considered along with the rule generation algorithm – Apriori. Analyzing the information available on sick and healthy individuals and taking confidence as indicator. Females are seen to have more chance of being free from coronary heart disease than males. It is also seen that factors such as chest pain being asymptomatic and the presence of exercise- induced angina indicate the likely of existence of heart disease for both men and women. On the other hand, the result showed that when exercise induced angina (chest pain) was false, it was a good indicator of a person being healthy irrespective of gender. This research has demonstrated the use of rule mining to determine interesting knowledge.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".