Heart Disease Prediction using Machine Learning

Abstract

Heart disease is a significant global health concern, accounting for a substantial number of deaths worldwide. Early prediction and detection of heart disease play a pivotal role in improving patient outcomes and reducing mortality rates. Machine learning techniques have demonstrated considerable potential in accurately predicting heart disease based on patient data. In this research paper, we propose a novel approach to heart disease prediction using machine learning algorithms, with a particular focus on creating a user-friendly graphical user interface (GUI) for enhanced accessibility and ease of use. The proposed approach leverages a diverse dataset encompassing demographic information, medical history, laboratory results, and diagnostic tests, providing a comprehensive view of a patient's health status. Multiple state-of-the-art machine learning algorithms, including logistic regression, support vector machines, random forests, and artificial neural networks, are employed to build robust prediction models. These models are trained, validated, and evaluated using appropriate performance metrics to ensure accuracy and reliability. To facilitate practical implementation, a user-friendly GUI is designed to provide an intuitive interface for healthcare professionals and individuals without extensive programming or machine learning expertise.