Machine Learning Based Heart Disease Prediction Using ECG Image

In a global health context outlined by the World Health Organization (WHO), the escalating prevalence of CVDs underscores a pressing need for advanced diagnostic methodologies. CVDs remain a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection. Traditional diagnostic methods face challenges in providing timely and accurate identification of heart conditions, often relying on subjective interpretation and lacking the sensitivity required for early intervention. Recognizing this critical gap, our engineering project focuses on the early detection of heart diseases using ML and DL techniques applied to ECG images. The integration of ML and DL offers a promising avenue to overcome the limitations of conventional diagnostic tools, leveraging intricate patterns within ECG data for more precise and timely detection. By addressing these challenges, our research aims to contribute to the advancement of cardiovascular health, providing a robust framework for early disease detection and improving overall patient outcomes.