Abstract:
Heart failure is a prevalent and life-threatening condition that affects millions of individuals worldwide. Early detection and proactive management of heart failure can significantly improve patient outcomes and reduce healthcare costs. In this project, we aim to develop a web-based heart failure prediction system that utilizes machine learning techniques to assist in the timely identification and risk assessment of heart failure. The system leverages a diverse dataset comprising patient demographics, medical history, laboratory results, and diagnostic test reports related to heart health. To make the heart failure prediction system accessible to healthcare providers and patients, a userfriendly web application is developed. The web application incorporates intuitive design principles and provides a seamless user experience. The trained heart failure prediction model is deployed as a web service or API, enabling the web application to communicate with the model and obtain predictions based on user input. Robust security measures are implemented to ensure the confidentiality and integrity of patient data, including encryption of data transmission, user authentication, and authorization mechanisms. The web-based heart failure prediction system is thoroughly tested and validated to ensure its accuracy, reliability, and usability. By developing a web-based heart failure prediction system, this project aims to contribute to the early detection and management of heart failure, potentially reducing the burden on healthcare systems and improving patient outcomes. The system holds promise as a valuable tool for healthcare providers and patients, empowering them to make informed decisions regarding heart health and proactive intervention strategies. i
