Heart Disease Prediction Using Adaptive Infinite Feature Selection and Deep Neural Networks
Bibliographic record
Abstract
Prediction of heart disease is one of the most important fields of study in modern science. By studying data such as cholesterol levels, blood sugar, and blood pressure, heart disease can be predicted. In recent years, several machine learning techniques have been used to aid in fast prediction by learning from the data. However, the prediction accuracy still remains low. This is due to lower number of records contained in the databases available. In this paper, we propose a new method of heart disease prediction using a modified variation of infinite feature selection and multilayer perceptron. The method shows a high accuracy of 87.70%, a high F1-score of 87.21%, a high sensitivity of 88.50%, a high specificity of 87.02%, and a high precision in prediction of 86.05%. on the Cleveland, Hungarian, Switzerland, Long Beach, and Statlog datasets. For evaluation purposes, we have combined all the datasets together and then divided the combined dataset into training and test samples with a 20 % percent of the samples allocated for testing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".