Diagnosis of Heart Disease using Fuzzy Logic

Abstract

Hospitals in both the developed and developing countries are faced with challenges in the treatment of patients with heartdiseases; reasons being lack of adequate medical information. nutritional fluctuation, pressure and languor are among several factors that have led to development of Corazon issues to an extent of becoming fatal, making it a life threatening issue at an alarming rate. Though early prediction of cardiac illnesses is a noble thought since it assists in the identification of a civilization disease, efforts that have applied data capture to pinpoint delicate relations between symptoms and diseases are often suboptimal. Consequently, activities such as medical diagnosis, which are normally within the domain of subject-matter specialists, still present a difficult endeavor. The purpose of this research study is to create a fuzzy knowledge base expert system for recognizing patient status, with the goal of meeting this essential need. This system employs fuzzy rule base and membership functions to optimally categories the risk factors associated with the organization through use of fuzzy logic. It has the potential to revolutionizes the process of diagnosing the disease and offer dependencies to the healthcare professionals for timely and appropriate identification of the heart risks. The rationale for this suggested remedy is described by a called fuzzy logic a computing that imitates human thinking by toleratingvagueness and uncertainty. Fuzzy logic embraces the vagueness that is seen in diagnoses, unlike binary logic forms that just work in true or false paradigms.