Designing an Expert System for the Diagnosis of Multiple Myeloma by Using Rough Set Theory

Abstract

Background: One of the most interesting and important topics in the field of information systems and knowledge management is the concept of eliciting rules and collecting the knowledge of human experts in various subjects to be used in expert systems. Many scientists have used decision support systems to support businesses or organizational decision-making activities, including clinical decision support systems for medical diagnosis. Objective: In this study, a rough set based expert system is designed for the diagnosis of one type of blood cancer called multiple myeloma. In order to improve the validity of generated models, three condition attributes that define the shape of “Total protein”, “Beta2%” and “Gamma%” are added to the models to improve the decision attribute value domain. Method: In this study, 1100 serum protein electrophoresis tests are investigated and based on these test results, 15 condition attributes are defined. Four different rule models are obtained through extracting rules from reducts. Janson and Genetic Algorithm with "Full" and "ORR" approaches have been used to generate reducts. Results: The GA/ORR of the information system with 87% accuracy is used as an inference engine of an expert system and a unique user interface is designed to automatically analyze test results based on these generated models. Gamma% is detected as a core attribute of the information system. Conclusion: Based on the results of generating reducts, the Gamma% attribute is detected as a core of the information system. This means that information, which is resulted from this conditional attribute, has the greatest impact on the diagnosis of multiple myeloma. The GA/ORR model with 87% accuracy is selected as the inference engine of the expert system and finally, a unique user interface is created to help specialists diagnose multiple myeloma.