Diagnosis of Arthritis Through Fuzzy Inference System

Authors:
Sachidanand Singh;Atul Kumar;K. Panneerselvam;J. Jannet Vennila
Affiliations:
Department of Bioinformatics, Karunya University, Coimbatore, India;Department of Bioinformatics, Karunya University, Coimbatore, India and , Lucknow, India 226010;Department of Bioinformatics, Karunya University, Coimbatore, India;Department of Bioinformatics, Karunya University, Coimbatore, India
Venue:
Journal of Medical Systems
Year:
2012

Citing 3
Cited 1

A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine

Expert Systems with Applications: An International Journal
An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

Digital Signal Processing
A genetic-fuzzy approach for mobile robot navigation among moving obstacles

International Journal of Approximate Reasoning

Fuzzy Assessment of Health Information System Users' Security Awareness

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process.