A Machine Learning-Driven Predictive Framework for Early Detection and Prevention of Cardiovascular Diseases in U.S. Healthcare Systems

Cardiovascular Disease, Early Detection, Machine Learning, Predictive Modeling, Healthcare Systems, Electronic Health Records, Prevention, Explainable AI, Public Health, U.S. Healthcare

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May 5, 2025

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Cardiovascular diseases (CVDs) remain the leading cause of death in the United States, accounting for approximately one in every five deaths annually. Despite the availability of advanced diagnostic tools and treatment options, early detection and prevention of CVDs remain significant challenges due to the complex interplay of genetic, behavioral, and environmental factors. This study proposes a machine learning-driven predictive framework aimed at enhancing early diagnosis and preventive interventions for CVDs within U.S. healthcare systems. The framework leverages large-scale electronic health records (EHRs), wearable device data, and socioeconomic variables to train predictive models capable of identifying high-risk individuals with greater accuracy and speed than conventional methods. Using supervised learning algorithms such as random forest, support vector machines, and gradient boosting, the proposed model was trained on publicly available and anonymized datasets, including the Framingham Heart Study and MIMIC-III. Feature engineering techniques were employed to extract critical indicators such as blood pressure, cholesterol levels, smoking status, physical activity, and family history. The framework achieved high predictive performance with an average area under the curve (AUC) exceeding 0.90, demonstrating robust classification of individuals at risk of developing CVDs. Furthermore, the model incorporates explainable AI (XAI) techniques to enhance transparency and facilitate clinician adoption, enabling actionable insights into modifiable risk factors. Integration with existing healthcare infrastructures is facilitated through a user-friendly dashboard, allowing for real-time risk assessment and patient stratification. This innovation not only enhances clinical decision-making but also aligns with national public health goals by supporting targeted prevention strategies, reducing healthcare costs, and improving patient outcomes. The study highlights the potential of machine learning in transforming cardiovascular healthcare delivery through proactive and personalized care. Future research will focus on expanding datasets to include more diverse populations and incorporating deep learning models for improved temporal pattern recognition in longitudinal data.