A Survey on A CNN Driven System for Early Age Heart Disease Prediction and Personalized Health Guidance

Cardiovascular disease remains significant contributors to global mortality, with many cases progressing silently because of lack of noticeable symptoms during the initial stages. To address the early diagnosis, this study presents a novel approach employing a CNN based framework for the prediction of heart disease risk using electrocardiogram (ECG) image data. By analysing ECG patterns, the proposed model is capable of identifying potential indicators of cardiac abnormalities even in asymptomatic individuals. In addition to risk assessment, the system offers tailored health recommendations, guiding patients with personalized advice on lifestyle modifications, preventive measures, and further clinical evaluation. This functionality not only allows users to make informed health decisions. Furthermore, the model is integrated with an UI designed for accessibility and ease of use by both medical professionals and patients. This integration facilitates seamless interaction with the system, ensuring timely intervention and support in clinical settings as well as for individual health monitoring.