Predictive Wellness: Exploring the Synergy of Machine Learning and E-Health in Heart Disease

Abstract

Predictive wellness has become a paramount focus in modern healthcare, leveraging the synergistic potential of machine learning (ML) and E-Health to revolutionize the prevention and management of heart disease. This paper delves into the intricate relationship between these two domains, exploring how ML algorithms can be harnessed to enhance early detection, risk assessment, and personalized interventions for heart disease patients. Through the amalgamation of extensive patient data, including clinical records, physiological measurements, and lifestyle factors, our study develops sophisticated predictive models using advanced ML techniques. These models not only aid in identifying individuals at high risk of developing heart disease but also provide actionable insights for healthcare practitioners to tailor interventions according to each patient's unique profile. Key to the success of our approach is the integration of E-Health platforms, facilitating seamless data collection, storage, and analysis. By harnessing the power of electronic health records (EHRs) and wearable devices, we enable continuous monitoring of patients' health status, empowering proactive interventions and real-time adjustments to treatment plans.