Hear Failure Prediction Using ML Techniquies

Abstract

The Heart Failure Prediction Project uses machine learning to predict the likelihood of heart failure by analyzing medical attributes such as age, cholesterol levels, blood pressure, and heart rate. Early detection enables timely medical intervention, reducing risks and improving outcomes. The project employs multiple machine learning models, including Logistic Regression, Random Forest, and Gradient Boosting, with ensemble learning techniques to enhance accuracy and reliability.
A Flask-based web application provides an intuitive interface for healthcare professionals to input patient data and obtain immediate predictions. Data preprocessing, such as handling missing values and scaling features, ensures model robustness, while evaluation metrics like accuracy and F1-score measure effectiveness.
This project demonstrates the potential of AI in healthcare by automating and improving diagnostic processes. Future work includes integrating real-time data from wearable devices, expanding datasets for better generalization, and deploying the system in cloud environments for global accessibility.