Cardiovascular Analytics: A Web-Based Deep Learning Approach to Heart Disease Diagnosis

Cardiovascular diseases remain a primary cause of global mortality, highlighting the need for timely and precise diagnosis. This paper, Cardiovascular Analytics: A Web-Based Deep Learning Approach to Heart Disease Diagnosis, introduces an advanced diagnostic platform that utilizes patient clinical and demographic information for swift heart disease prediction. A feedforward neural network created with TensorFlow functions as the principal prediction model, providing enhanced diagnostic precision relative to conventional machine learning techniques like Support Vector Machines (SVM) and Decision Trees. The platform incorporates a contemporary web architecture featuring a FastAPI backend, a Next.js frontend, and a PostgreSQL database to guarantee efficient processing, secure data storage, and user-friendly access. Role-based access control permits patients to upload and oversee their health records, whereas medical professionals can examine diagnostic results and patient histories. This system integrates deep learning with an interactive web framework to improve the accuracy, accessibility, and efficiency of cardiovascular disease detection, offering a scalable solution for practical clinical applications