Rule-based Expert System For Diagnosis Research Articles

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

The majority of people have experienced pain in their low back or neck in their lives. In this paper a type-2 fuzzy rule based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in five categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Using of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians’ diagnosis. The outputs of the system are comprised of type of disorder, location and severity as well as the necessity of taking a M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

TUBERDIAG: AN EXPERT SYSTEM FOR PULMONARY TUBERCULOSIS DIAGNOSIS

In this paper, we present an overview of TUBERDIAG, a rule-based expert system for diagnosis of pulmonary tuberculosis. This system was developed as a 2-year joint project by the Institute of Information Technology (National Center for Science and Technology) and the National Institute of Tuberculosis and Lung Diseases, Hanoi. After designing and building a suitable inference engine for this system, we have done a lot of work to create an effective knowledge base; at present, this knowledge base contains more than 1,000 rules. The paper focuses on how the rule base is constructed, managed, and used. We also present here the first evaluation of TUBERDIAG by the doctors at the National Institute of Tuberculosis and Lung Diseases, who have been playing a very important role in the project – providing the system with their knowledge.

047 Rule-based expert system for diagnosis of energy distribution in steel plant

047 Rule-based expert system for diagnosis of energy distribution in steel plant

Rule-Based Expert System for Diagnosis of Energy Distribution in Steel Plant

In this paper, a rule-based expert system for diagnosing energy distribution in a steel plant is developed to maintain an intelligent balance between the supply and the demand of gases being used as major energy sources in the plant. About 300 rules are incorporated into the expert system to imitate the action of the operators at the energy center. The expert system draws a conclusion by inference through the rules using the data obtained from the sensors or the gauges installed in several unit plants. It then displays the results of the inference on the CRT in the energy center to guide the operators. To infer conclusions, a commercial expert system shell Nexpert Object is used on a SUN 4 workstation.