A Novel Hybrid Model Combining Feature Selection and Imbalance Handling for Prediction of Heart Failure Survival
Bibliographic record
Abstract
The imbalance and irrelevant or redundant features often hinder the performance of accurate prediction models.This can aid healthcare professionals in the early detection and intervention of heart failure (HF).HF is a critical cardiovascular condition that poses a significant risk to human health, frequently resulting in high mortality rates if not diagnosed and managed early.In this paper, we propose an enhanced prediction approach for HF survivors based on a hybrid model (hybrid HF) that integrates feature selection techniques with the resampling technique-synthetic minority oversampling technique combined with edited nearest neighbors (SMOTEENN).SMOTEENN effectively addresses the class imbalance problem by synthesizing new minority class instances and improving noisy samples after selecting the most relevant features from the data, thereby enhancing the quality of the training data.Additionally, applied feature selection to identify the most relevant predictors, reducing model complexity and enhancing interpretability.Experimental results demonstrate that our hybrid HF model outperforms existing methods by uniquely integrating SMOTEENN with feature selection to achieve 0.9315 accuracy in predicting a heart patient's survival, improving model accuracy by 8% compared to the baseline methods for the RF algorithm.Finally, improving classification, sensitivity, and overall model robustness compared to the baseline method.
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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 | 0.001 | 0.001 |
| 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 | 0.002 | 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".