This collaborative project, "Heart Disease Prediction Using Machine Learning," was undertaken by a dedicated team of four individuals: Garima Paudel, Anisha Silwal, Aayushma Paudel, and Nisha Pokharel.

Revolutionized cardiovascular health with a logistic regression-based heart disease prediction model. Streamlining diagnostics, it saves valuable doctor time by swiftly analyzing patient data, offering proactive insights for efficient and informed medical decisions.

CARDIOsetu is a web application designed to monitor individual heart health. It uses API integration to enable voice-to-text input for accessibility, making it easier for individuals with verbal and visual disabilities to interact with the app.

A predictive analytics project leveraging machine learning algorithms to forecast the likelihood of heart disease in patients. Heart Disease Prediction Using Machine Learning Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

SEHAT is an interactive Predictive Health Application using Streamlit and machine learning models to forecast diabetes and heart disease risks based on user health parameters. Enhance your health awareness with immediate insights and proactive management tools.

Heart disease is one of the leading causes of death worldwide. Early detection and prediction of heart disease can significantly improve treatment outcomes and save lives. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials