Enhancement of Diagnosis System for Tuberculosis Using Case-based Reasoning

Abstract

Case-based Reasoning (CBR) is a recent approach to problem solving and learning that has got a lot of attention over the last few years. CBR is an Artificial Intelligence method based on a plausible cognitive model of human reasoning. People take a great interest in computer and then computer-based methods are increasingly used to improve the quality of the medical services. CBR is considered established method for building medical diagnosis systems. Traditional expert systems model human problem solving as a deductive process. They construct a solution by applying general rules to the description of a problem. It becomes apparent that human experts rely heavily on memory of past cases when solving problems. In this paper, a system is presented using CBR and decision tree algorithm for medical diagnosis. It is implemented for the efficient diagnosis of tuberculosis. This system is proposed as a development tool. The main feature of the proposed system is to provide a simple and integrated tool for designing diagnostic applications. This system also provides for helping the Tuberculosis disease and controlling the treatments for patients. So that, every user can do the diagnosis as a physician.