Classification and Diagnosis of Heart Disease Using Machine Learning

Abstract Heart disease is a common and serious disease that causes many deaths around the world. The study aims to explore the use of machine learning techniques in classifying and diagnosing heart diseases and to develop a system capable of diagnosing and classifying different types of heart diseases using machine learning techniques. A number of algorithms commonly used in healthcare, such as Naive Bayes model, SVM, k-nearest neighbor (k-NN), and others, were reviewed. The study points out the importance of the quality of the data used in the database to obtain an accurate and reliable diagnosis. Data were collected from patient records in hospitals and clinics, analyzed and compared with previous relevant studies. Clinical decision assistance software has been used to help make medical decisions based on patient information. Positive results have been achieved that confirm the effectiveness of using machine learning techniques in diagnosing heart diseases. These technologies have shown the potential to improve the accuracy and efficiency of diagnosis, leading to improved patient outcomes and reduced health burdens. It also concluded the need to develop effective diagnostic tools and enhance the prevention of heart disease. The study is an important foundation for healthcare professionals and doctors working in the field of cardiology, as the techniques used can help them better understand and diagnose conditions and improve patient care.