Published Paper Details:

Millions of people around the world are suffering from cardio vascular diseases(CVD) and has the highest mortality rate among other diseases. It is essential to develop a system that can accurately predict the chance of getting a CVD. Developments in the fields of AI and Machine Learning are helpful in creating an effective prediction system. Existing systems either use numeric data or ECG(Electro Cardio Gram) graph for predictions.Despite significant progress in these individual domains, there remains a gap in the literature concerning the integration of numerical and image datasets for comprehensive CVD risk assessment. The proposed system aims to bridge this gap by introducing an innovative methodology that seamlessly integrates both numerical and image datasets to enhance CVD risk assessment. By combining numerical datasets extracted from EHRs with image datasets obtained from ECG recordings, the proposed system aims to provide a comprehensive analysis of patient health status, leveraging both clinical measurements and insights from ECG images.