Using Machine Learning Algorithms for Assisting Heart Disease Diagnosis

Abstract

Heart disease continues to be one of the top causes of death around the world, which highlights the importance of catching it early to ensure timely treatment. This study explores the use of various body health information, such as Age, Gender etc. and physiological responses such as chest pain, shortness of breath, Fatigue, Dizziness etc. The meaning of this study is to find out how does different body responses correspond to if the patient catches heart disease and which kind of method of diagnosis would be more accurate and efficient. This research investigates applying various types of classifier models finding the better performance in heart disease diagnosis. Kaggle data set is used in this research. To evaluate the performance of different clarification models to choose the most validate model and analyze or determine important features to help doctors’ diagnosis. At the end, the study found out that models including SVC and Random Forest model are the accurate ones while studying the data.