Heart disease Prediction and detection using Machine Learning Algorithms

Abstract

This paper focuses on building a heart disease detection system using machine learning. A Python- based application is developed for healthcare research, offering reliability and versatility. The process involves handling categorical data, collecting databases, KNN, and attribute evaluation. A KNN is introduced for improved accuracy, around 96.58%, in identifying heart diseases. The algorithm's experiments and outcomes are discussed, highlighting enhanced research diagnosis accuracies. The paper concludes by summarizing objectives, limitations, and research contributions . Cardiovascular diseases (CVDs) are a leading cause of global morbidity and mortality. Early detection of heart diseases is crucial for effective intervention and prevention. The existing diagnostic methods often involve complex and expensive procedures. Therefore, there is a pressing need to develop a robust and accessible Heart Disease Detection System using Machine Learning (ML) algorithms