A fuzzy expert system for response determining diagnosis and management movement impairments syndrome

Diagnosis is a very important aspect of medical care. Expert systems are developed to make the skills of specialists available for non-specialists. These systems simulate human thinking and performance and approach the performance of expert systems to the performance of a human expert. The purpose of this study is to develop a fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in elbow and shoulder. A fuzzy Delphi method is used to gather data related to symptoms and treatments. By knowledge acquisition, an expert system is developed. Components of the proposed system consist of a knowledge base, fuzzy inference engine, working memory, user interface and knowledge acquisition utilities. The developed system is able to diagnose 18 disorders of the elbow and the shoulder. To compare systemic diagnosis and expert diagnosis, SPSS software is used for statistical analysis. Because 26 out of 30 patients had a systemic diagnosis similar to the expert diagnosis, it can be concluded that 86.7% of systemic diagnoses are similar to expert diagnosis. In the absence of experts, this intelligent application can provide reliable diagnosis and treatment. Application of intelligent and semi-intelligent systems such as expert systems can aid the users to make decisions.