Evaluation of Medical Biomarkers in Machine Learning Models for Classification of Heart Failure with Preserved and Reduced Ejection Fraction

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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