Type-2 fuzzy rule-based expert system for Ankylosing spondylitis diagnosis

Abstract

Ankylosing spondylitis (AS) is a chronic inflammatory disease. The pathogenesis of AS is poorly understood. However its association with human leukycyte antigen (HLA)-B27 is known. AS causes significant pain, disability, and social burden around the world [1]. Early diagnosis and treatment of AS are necessary in order to prevent or reduce all types of costs associated with loss of function. If the diagnosis is missed, however, the quality of patient's life will degrade. Besides, as a positive family history of AS is a strong risk factor for the disease, this negligence will put other family member's in jeopardy.Nowadays, Expert systems play a big role in diagnosis of patients with different diseases. The application of expert system to diagnose diseases started in the 70s with the development of Mycin. Expert systems in medical diagnosis can help in storing more knowledge than before and make it accessible in absence of a specialist and increase distribution of expertise.Our goal in this paper is to design a type-2 fuzzy rule-based expert system for AS diagnosis where the rules are evidencebased. The basic aim of evidence-based practice is to establish a narrow set of criteria for diagnosis based on research studies. System has mainly two parts. Firstly, the suspicion of disease is assessed for the persons identity according to odds ratio studies and patient's family history. Then the modified New York criteria (1984) for exploring sign and symptoms and the HLA-B27 examination result are considered.The system benefits from fuzzy reasoning and can manage the uncertain inputs through fuzzifying them and making use of type-2 fuzzy rules. Moreover, the system is connected to a spreadsheet for storing the patient's input data and system's final diagnosis. This system can be used by a non-rheumatologist in the diagnosis of AS or by a rheumatologist as an assistant.

Diagnosis of heart diseases: A fuzzy-logic-based approach

“…It has already been shown the application of fuzzy logic and its effectiveness in the medical diagnosis of Ankylosing Spondylitis, anemia, dengue, thyroid, Alzheimer, Blood pressure, diabetes, and mental health, ontology-aided food and drug recommendation systems for a patient, etc. for identifying various disorders [5,[9][10][11][12][13][14][15]. Similarly, a lot of research is also going on to create an accurate, economical, and effective fuzzy logic-based expert system for diagnosing heart disease because the world health organization (WHO) reported that cardiovascular diseases (CVD) are now the main cause of death worldwide [5,[16][17][18][19].…”

“…for identifying various disorders [5,[9][10][11][12][13][14][15]. Similarly, a lot of research is also going on to create an accurate, economical, and effective fuzzy logic-based expert system for diagnosing heart disease because the world health organization (WHO) reported that cardiovascular diseases (CVD) are now the main cause of death worldwide [5,[16][17][18][19]. Interestingly, the chance of mortality can be decreased by early diagnosis of heart diseases.…”

“…A fuzzy expert system is a collection of fuzzy rules means knowledge base and membership functions that are used to manage data. A fuzzy logic-based expert system is widely used in the domain of medical diagnosis where there are numerous variables and uncertainties that affect the decision process thereby causing differences in the opinion of the practitioners [ 2 – 5 ]. In the medical application domain, there are usually imprecise conditions, and therefore fuzzy methods seem to be more suitable than crisp ones.…”

A practical review and taxonomy of fuzzy expert systems: methods and applications

“…In addition to the information provided in Table V, Asl and Zarandi (2017) and Maftouni et al . (2015) have also designed several Type-2 fuzzy expert systems documented in conference papers.…”