Malaria is a deadly disease that claims yearly lives of millions in Africa, and other endemic continents. The prevalence of malaria in these endemic regions is majorly attached to the lack of competent medical experts who are capable of providing medical care for the affected victims. This study considers developing a mobile based fuzzy expert system that could assist in diagnosing malaria. The fuzzification of crisp inputs by the system was carried out using an inter-valued and triangular membership functions while the deffuzification of the inference engine outputs was performed by weighted average method. Root sum square method of drawing inferences has been employed while the whole development has been achieved with the help of Java 2 Micro Edition of Java. This expert system executes on the readily available mobile devices of the patients. This fuzzy system was finally evaluated and confirmed effective in providing a human-expert like diagnosis.

The high rate at which Africans die of syphilis yearly has been majorly attributed to the uneven ratio of the patients to competent medical practitioners who provide Medicare. This mortality rate has always drawn the attention of researchers and different approaches had been used to bring the rate down. This paper provides a software solution that personifies the expert-like way of providing diagnostic service to patients who suffer this disease. It is capable of making approximate diagnosis based on uncertainties. The system has been structured into five components: user interface, fuzzification, knowledge base, inference engine and defuzzification. The user interface uses a graphic user interface based method of human-computer interaction while the fuzzification component has transformed crisp quantities into fuzzy quantities using both interval-valued and S-curve membership functions. The reasoning has been achieved using root sum square (RSS) method and transformation of fuzzy values to scalar ones was through weighted average method. This system was tested and found effective.