Elevating E-Healthcare: Machine Learning Insights into Heart Disease Identification


  • Nadeem Ahmad Department of Computer Science, University of California, USA


E-Healthcare, Machine Learning, Heart Disease, Identification, Predictive Model, Risk Factors, Interpretability, Preventive Healthcare


E-Healthcare has emerged as a pivotal domain in transforming traditional healthcare systems, leveraging technology to enhance efficiency and precision. This paper focuses on the integration of machine learning (ML) algorithms to elevate the identification and diagnosis of heart disease, a leading cause of global morbidity and mortality. Through the utilization of extensive datasets encompassing diverse patient profiles, our study employs state-of-the-art ML techniques, including supervised learning and deep neural networks, to develop a robust predictive model. E-Healthcare is undergoing a transformative phase, leveraging technology to enhance efficiency and precision. This paper explores the integration of machine learning (ML) algorithms for the identification of heart disease, a major global health concern. Utilizing extensive datasets and advanced ML techniques, a predictive model is developed, incorporating clinical and demographic features to analyze risk factors. The study also emphasizes interpretability, aiding healthcare professionals in understanding and trusting the model. Real-world experiments validate the model's efficacy, showcasing its superiority over traditional diagnostic methods. The research highlights the potential of ML in revolutionizing preventive healthcare, enabling early intervention and personalized treatment plans.