In the Heart of E-Healthcare: Machine Learning for Disease Identification and Prevention
Keywords:
E-Healthcare, Machine Learning, Disease Identification, Prevention, Predictive Models, Supervised Learning, Deep Neural Networks, Risk Factors, Interpretability, Transparency, Healthcare Professionals, Timely Interventions, Personalized HealthcareAbstract
The intersection of technology and healthcare has given rise to E-Healthcare, a dynamic field with the potential to revolutionize disease identification and prevention. This paper delves into the core of E-Healthcare, exploring the pivotal role of machine learning in advancing these objectives, with a particular focus on heart disease. Machine learning algorithms have become instrumental in processing vast datasets to develop predictive models for disease identification. Our research leverages state-of-the-art techniques, including supervised learning, to analyze comprehensive sets of clinical and demographic features. By integrating deep neural networks, our model achieves a nuanced understanding of risk factors associated with heart disease, contributing to early and accurate identification. The interpretability of the machine learning model is a crucial aspect of our study. We shed light on the influential features driving predictions, enhancing transparency for healthcare professionals. This not only fortifies trust in the model but also empowers medical practitioners with insights into the factors shaping patient risk profiles.
