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Journal of Artificial Intelligence
  Year: 2011 | Volume: 4 | Issue: 4 | Page No.: 269-278
DOI: 10.3923/jai.2011.269.278
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Application of Knowledge Based System for Diagnosis of Osteoarthritis

T.F. Blessia, S. Singh, A. Kumar and J.J. Vennila

Osteoarthritis (OA) is a chronic condition characterized by the degeneration of cartilage in the joints. Some of the symptoms of osteoarthritis include joint pain, stiffness, swelling, bony outgrowths and grating sensation. There are many factors to diagnose osteoarthritis. To help the experts identify the possible errors, classification systems provide medical data to be examined in detail in short time. Expert system and differential artificial intelligence techniques for classification systems in medical diagnosis of arthritis are increasing gradually. The recent popularity of fuzzy expert systems, in particular fuzzy controllers, has created the need for automation of not only the process of extraction of fuzzy rules but also the process of generating the parameters of the associated fuzzy sets. The study reports the application of fuzzy logic inference system to automate the knowledge acquisition for diagnosis of osteoarthritis. These techniques are dealing with inexact and imprecise problem domains and have been demonstrated to be useful in the solution of classification problems. It addresses the issue of the application of appropriate evaluation criteria such as rule based accuracy and comprehensibility for new knowledge acquisition techniques. A system for the diagnosis of osteoarthritis and its severity using Fuzzy Logic has been designed so that the common people, who suspects little bit of pain in joints, may use this system and get the result on the diagnosis and severity of osteoarthritis which will be helpful to guide them to take proper curative measures before the severity increases.
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How to cite this article:

T.F. Blessia, S. Singh, A. Kumar and J.J. Vennila, 2011. Application of Knowledge Based System for Diagnosis of Osteoarthritis. Journal of Artificial Intelligence, 4: 269-278.

DOI: 10.3923/jai.2011.269.278






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