FRBF: A Fuzzy Rule Based Framework for Heart Disease Diagnosis

Abstract

Heart disease is also known as cardiovascular disease. It is one of the most dangerous and deadly disease in all over the globe. Cardiovascular disease was deemed as a major illness in old and middle age, but recent trends shown that now cardiovascular disease is also a deadly disease in young age group due to irregular habit. However, Angiography is one of the way to diagnose heart disease, but it is very expensive and also has major side effect. The aim of this research paper is to design a fuzzy rule based framework to diagnosis of the risk level of the heart disease. Our proposed framework used a Mamdani interface system and used UCI machine repository dataset for heart disease diagnosis. In this proposed study, we have used 10 Input attribute and one output attribute with 554 rules. Besides, a comparative table is also presented, where proposed methodology is better than other methodology. According to the proposed methodology results, that the performance is highly successful and it is a promising tool for identification of a heart disease patient at an early stage. We have achieved accuracy, sensitivity rates of 95.2% and 87.04 respectively, on the UCI dataset.