Improving Cardiovascular Disease Prognosis Using Outlier Detection and Hyperparameter Optimization of Machine Learning Models
Bibliographic record
Abstract
Cardiovascular diseases, globally recognized as prominent contributors to morbidity and mortality, have led to an imperative demand for precise, accessible, and efficient diagnostic methodologies.This study introduces a hybrid classification system integrating an ensemble model and a Fuzzy C Means-based neural network with the objective of augmenting predictive accuracy.A comparative analysis on scalar standards was undertaken to determine the optimal feature scaling technique, thereby enhancing predictive proficiency while optimizing time efficiency.The study further incorporates Random Forest, Support Vector Machines, k-Nearest Neighbor, and deep learning models into the diagnostic framework, while employing a confusion matrix as a performance evaluation tool.The GridsearchCV technique is utilized for hyperparameter optimization, its influence on the accuracy of machine learning (ML) models is critically examined.Special attention is given to the role of outliers and their manipulation using supervised ML algorithms, investigating the impact of outlier exclusion on model accuracy.The experimental data was sourced from a cardiovascular patients dataset in the UCI Machine Learning Repository.The findings of the study suggest that the proposed classifier ensemble model surpasses comparable advancements, achieving an exemplary classification accuracy of 98.78%.This paper thus contributes to the evolving landscape of ML application in cardiovascular disease prediction, emphasizing the significance of outlier detection and hyperparameter optimization.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".