A Survey Techniques Used for Prediction of Heart Attack with Machine Learning and Medical Text Mining

Abstract

Heart attack is one of the most critical heart disease in the world and affects human life very badly. In heart attack, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart attack is important for heart failure prevention and treatment. The diagnosis of such condition through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart attack causes and related problems, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart attack prediction by using heart disease dataset. We used popular machine learning algorithms for performance evaluation metrics such as classification accuracy, sensitivity and correlation coefficient. The proposed system can easily predict and classify people with heart attack possibilities from healthy people.