Heart Health Predictions: A Comprehensive Analysis of Machine Learning in E-Healthcare


  • Khalid Husain Department of Computer Science, university of Sargodha, Pakistan


E-Healthcare, Machine Learning, Heart Health Predictions, Predictive Diagnostics, Supervised Learning, Deep Neural Networks, Clinical Features, Early Identification, Personalized Treatment, Conventional Diagnostic Methods


As the intersection of healthcare and technology evolves, the integration of machine learning (ML) in E-Healthcare stands out as a promising avenue for advancing predictive diagnostics. This paper delves into the realm of heart health predictions, employing a comprehensive analysis of ML techniques to enhance accuracy and efficiency in disease identification. Our study utilizes diverse datasets encompassing a wide array of patient profiles, incorporating supervised learning and sophisticated deep neural networks. The resulting predictive model considers a myriad of clinical and demographic features, providing a holistic assessment of potential risk factors associated with heart disease. Transparency and interpretability are pivotal aspects of our approach, shedding light on the intricate relationships between features and predictions. This not only fosters trust in the model but also equips healthcare professionals with valuable insights into the factors influencing a patient's heart health. Validation experiments conducted on real-world datasets underscore the superiority of our ML-based approach over conventional diagnostic methods. The integration of machine learning not only enhances accuracy but also allows for early identification of heart disease, enabling timely interventions and personalized treatment strategies.