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Designing medical expert system based on logical reduced rule for basic malaria diagnosis from malaria signs and symptoms

Murat Selek (Technical Vocational School of Higher Education, Selcuk Universitesi, Konya, Turkey)
Fatih Basçiftçi (Department of Computer Engineering, Selcuk Universitesi, Konya, Turkey)
Serkan Örücü (Department of Computer Technology and Computer Programming, Ermenek Vocational School, Karamanoglu Mehmetbey Universitesi, Karaman, Turkey)

World Journal of Engineering

ISSN: 1708-5284

Article publication date: 12 June 2017




Malaria is one of the most sinister life-threatening disease and generally transmitted by the bite of an Anopheles mosquito which was infected. These mosquitoes carry the Plasmodium parasite. Worldwide risk of malaria thread is very hard to deal, because of extreme temperature and climate changes which lead to uncontrolled changes in the mosquito population, as many deaths from malaria occur outside the healthcare system and other infections might be misdiagnosed as malaria unless a diagnostic test is done. The purpose of this study is creating a system which is early diagnosing malaria for settlements adequate healthcare units and non-immune travellers.


In this study’s system, the authors developed a new medical expert system (MES) process using the decreased rule base to detect malaria. The authors’ purpose was to successfully identify the illness by taking all symptoms of malaria into consideration in the MES (six basic signs, 64 different conditions). In the proposed MES process, in place of inspecting all the malaria-related signs, the authors used the decreased rule bases.


So as to take the lessen decreased bases, Boolean functions are used in a two-level simplification method. Using this method, decreased cases were evaluated by taking six symptoms of malaria into account instead of assessing 64 individual conditions.

Research limitations/implications

The system can be used in diagnosis of asthma and chronic obstructive respiratory disease.

Practical implications

The system can be used in absence of adequate healthcare units. Thus, malaria can be diagnosed early.


The authors hope that the system they have developed will be useful for settlements in the absence of adequate healthcare units and non-immune travellers.



This work is supported by Selçuk and Karamanoğlu Mehmetbey Universities Scientific Research Projects Coordinatorships, Konya, Karaman, Turkey.


Selek, M., Basçiftçi, F. and Örücü, S. (2017), "Designing medical expert system based on logical reduced rule for basic malaria diagnosis from malaria signs and symptoms", World Journal of Engineering, Vol. 14 No. 3, pp. 227-230.



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