MACHINE LEARNING-BASED PREDICTION OF MORTALITY RISK IN HEART FAILURE PATIENTS USING CLINICAL DATA
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Abstract
Heart failure remains a leading cause of morbidity and mortality worldwide, necessitating effective predictive models for early risk stratification. This study aims to develop and evaluate machine learning models for predicting mortality risk in heart failure patients using clinical data. A retrospective analysis was conducted on a publicly available dataset comprising 299 patients with demographic, clinical, and laboratory variables. Three supervised machine learning algorithms—Logistic Regression, Random Forest, and Support Vector Machine (SVM)—were implemented and evaluated using multiple performance metrics, including accuracy, precision, recall, F1-score, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC).The results demonstrated that Logistic Regression achieved the highest accuracy (0.80) and precision (0.93), while Random Forest showed the highest ROC-AUC (0.830), indicating strong discriminative ability across models. However, recall values were moderate across all models, highlighting challenges in identifying high-risk patients. Confusion matrix analysis revealed the presence of false negatives, emphasizing the need for improved sensitivity in clinical applications. Feature importance analysis identified follow-up time, serum creatinine, and ejection fraction as the most significant predictors of mortality, reflecting the combined influence of disease progression, renal dysfunction, and cardiac performance.Overall, the findings indicate that machine learning models can effectively predict mortality risk in heart failure patients using clinical data. However, further optimization and validation are required to enhance sensitivity and ensure reliable clinical application.
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