Fuzzy rule based expert system for diagnosis of multiple sclerosis

Multiple Sclerosis (MS) is an autoimmune disease in which insulating covers of nerves called myelin sheath are damaged. Myelin sheath helps the transmission of the nerve impulses. Damage to the myelin in the central nervous system (CNS) disrupts the communication between the brain and spinal cord and other parts of the body, thus cause a wide range of signs and symptoms. Therefore, it can be difficult to diagnose by physicians in some cases. Recently automated systems have been introduced for the diagnosis of some of the neurological disorders including Multiple Sclerosis. An important issue that should be considered in these automated systems is the fact that diagnosis process often confront with uncertainty and vagueness. Therefore, we determine to bring these uncertainties in our system by using Fuzzy Logic, for first time. Another weakness seemed in previous works, is their knowledge bases and reasoning process. This paper presents a fuzzy rule-based expert system for MS diagnosis. Decision making in this system is performed based on the person´s identity, symptoms and signs. In study of the cases mentioned, we confront with crisp variables that receive binary value. These crisp variables can lead to uncertain results. Fuzzy reasoning is used to address the uncertainties exist in diagnosis process. This system can help to non-neurologists in the diagnosis of MS or can be used as a neurologist physician assistant. The proposed system uses a spreadsheet for storing or extracting the information of the patients. System´s knowledge base built based on direct approach and the inference is done using forward-chaining method because of the multiplicity of factors that refers to MS.