Integrating machine learning techniques for enhanced prognostic modeling of heart failure risk in the american population

Abstract Heart failure remains a significant public health challenge, with high rates of morbidity and mortality. Early detection is critical for effective management, yet traditional diagnostic approaches often lack precision and are prone to variability. This study aims to develop a machine learning model specifically designed to predict heart failure onset with high accuracy, enabling timely intervention and personalized treatment strategies. Using a comprehensive dataset of U.S. residents, we analyzed various clinical and laboratory parameters to train and evaluate machine learning algorithms, including Logistic Regression, Random Forest, Naïve Bayes and XGBoost. The top-performing model was selected based on key evaluation metrics— F1-score, Sensitivity, Specificity and the confusion matrix— ensuring a balanced assessment of its predictive capability. By identifying high-risk individuals before symptoms escalate, this model could allow healthcare providers to proactively manage heart failure, potentially reducing hospitalizations and enhancing quality of life. The findings underscore the potential of machine learning to transform heart failure care, supporting a shift toward more data-driven and preventative healthcare in the United States.