Evaluation of Medical Biomarkers in Machine Learning Models for Classification of Heart Failure with Preserved and Reduced Ejection Fraction
Bibliographic record
Abstract
This study evaluates the effectiveness of various non-echocardiographic medical biomarkers, integrated with machine learning (ML) models, for classification of different types of Heart Failure (HF), specifically heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF). This differentiation is critical due to the distinct pathophysiology and management strategies required for these heart failure subtypes. A retrospective clinical dataset was collected from three medical centers in Serbia, comprising anonymized records of 481 heart failure patients ($221 \text{HFrEF}, 260 \text{HFpEF}$). The dataset included three primary types of variables: health records, laboratory test results, and electrocardiogram (ECG) data. Data preprocessing involved standardization of laboratory values and Multivariate Imputation by Chained Equations (MICE) to address missing parameters. Five different machine learning algorithms were employed, with an$80 / 20$train-test split and exhaustive Grid Search for hyperparameter optimization: Decision Tree, Random Forest, eXtreme Gradient Boosting Tree (XGBoost), Multilayer Perceptron (MLP), and Gradient Boosting Tree. Model performance was assessed using accuracy, precision, recall, and$\mathbf{F 1}$-score. The results indicate that ensemble methods, specifically Gradient Boosting Tree and Random Forest, consistently achieved superior performance across various data subsets. Notably, the combination of Health Records and ECG data yielded the highest predictive performance, with the Gradient Boosting Tree model achieving an F1-score of 0.7736 and a recall of 0.8039. Conversely, models trained predominantly on, or solely with, laboratory testing features consistently exhibited lower performance, suggesting that a broad inclusion of these biomarkers may introduce noise or redundancy for this specific classification task. These findings demonstrate that non-echocardiographic biomarkers can be effectively leveraged by machine learning models to differentiate between HFrEF and HFpEF, offering a promising diagnostic tool.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".