A Machine Learning Ensemble Classifier for Cardiovascular Disease Taxonomy
Bibliographic record
Abstract
This Paper presents an application of Machine Learning in cardiology and the role of ensemble classifiers for Cardiovascular Disease (CVD) taxonomy.The dataset from Kaggle on CVD was used.Data was cleaned and 5 feature reduction techniques were investigated.Furthermore, a statistical unbiased ensemble feature reduction is proposed by imposing a unitary weight on intersecting features.Considering only 7 features, the Recurrent Feature Elimination and the proposed unbiased-ensemble feature reduction techniques were effective for reducing variables.Here, 6 feature reduction methods are considered.Hence, from each feature reduction method; the diverse selected features are then fed into a set of 5 independent ML techniques to compose a corresponding classifier.This ML approach in turn considers the 5 resultant classifiers and one additional proposed Ensemble Classifier based on those 5 classifiers.This proposed Ensemble Classifier consisted of: Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k-Nearest Neighbor (KNN), classifiers.The output of the Machine Learning (ML) Classifiers approach is a classification/taxonomy to determine an individual with cardiovascular disease; or an individual that is free from cardiovascular disease.By considering the effective Recursive Feature Elimination method and the proposed Ensemble Classifier it was demonstrated that the body weight of an individual, systolic and diastolic blood pressure, cholesterol level, glucose level, level of physical activity, and the age are decisive in diagnosing the CVD condition of an individual.It is relevant to mention that a genetic feature was not available from the considered database; therefore, this potentially important factor was not considered in this study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".