Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis
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
One of the most important applications of machine learning systems is the diagnosis of heart disease which affect the lives of millions of people. Patients suffering from heart disease have lot of independent factors such as age, sex, serum cholesterol, blood sugar, etc. in common which can be used very effectively for diagnosis. In this paper an Extreme Learning Machine (ELM) algorithm is used to model these factors. The proposed system can replace a costly medical checkups with a warning system for patients of the probable presence of heart disease. The system is implemented on real data collected by the Cleveland Clinic Foundation where around 300 patients information has been collected. Simulation results show this architecture has about 80% accuracy in determining heart disease.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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