A Decision Support System for Tuberculosis Diagnosis.
Y. H. Kimbi · 2011
In this paper, a fuzzy expert system for tuberculosis diagnosis was developed for providing decision support platform to tuberculosis researchers, physicians, and other healthcare practitioners in tropical medicine. The combination of inadequate expertise and sometimes the complexity of medical practices exponentially increase the morbidity and mortality rates of tuberculosis patients. The task of arriving at an accurate medical diagnosis may sometimes become very complex and cumbersome. Fuzzy logic technology provides a simple way to arrive at a definite conclusion from vague, imprecise and ambiguous medical data. In order to achieve this, a study of the knowledge base system for tuberculosis was undertaken and the system was developed using fuzzy logic technology. The developed system composed of four components which include the knowledge base, the fuzzification, the inference engine and defuzzification components. The fuzzy inference method employed in this research is the Root Sum Square (RSS). Triangular membership function was used to show the degree of participation of each input parameter and the defuzzification technique employed in this research is the Center of Gravity (CoG). The fuzzy expert system was designed based on clinical observations, medical diagnosis and the expert’s knowledge. We selected 30 patients with tuberculosis and computed the results that were in the range of predefined limits by the domain experts.