Iraq's major infectious disease diagnosis using a fuzzy rule-based system


  • Zainab T. Al-Ars
  • Abbas Al-Bakry



Expert System, Knowledge Base, Diseases & Symptoms, Medical Diagnosis, Rule-based System.


The World Health Organization (WHO) declared that over 15 million people die annually due to infectious diseases at an average of 50,000 deaths per day. In the past two centuries, about 30 new cases have emerged and are threatening the health of millions of people currently. For most of these diseases, there is no available treatment strategy or vaccine currently. The WHO report warned that some of the major infectious diseases such as malaria, cholera, and hepatitis are a challenge in several countries despite the concerted efforts on their prevention and treatment. In this work, a fuzzy rule-based system was designed for the diagnosing of some major existing infectious diseases in Iraq and described the ways of detecting new ones. Diagnosis is often based on the symptoms that can be seen or felt. The medical advising system helps physicians or experts to make an appropriate disease diagnosis. Infection diseases have several symptoms and some of these symptoms are similar, thereby, creating several difficulties for accurate decision or diagnosis. In this study, the knowledge used was acquired through expert interviews and presented by the production rule (IF-THEN method). Fuzzy logic has been proven as a great tool for building intelligent decision-making knowledge-based systems and expert feedback. The results achieved in this study indicated a promising diagnosis performance of the system as it achieved 85.7% F-Score and 82.3% accuracy. This system will help in saving many lives in the rural communities where health care centers and expert physicians are limited.




[1] Ala'din A., "Health in Iraq: The Current Situation, Our Vision for the Future and Areas of Work". 2nd Edition. WHO? Ministry of Health, 2004.

[2] Octavian Arsene, Ioan Dumitrache and Ioana Mihu, "Expert System for Medicine Diagnosis Using Software Agents", Elsevier. Expert Systems with Applications. vol.42, no.4, 2015, pp. 1825–1834.

[3] Abu-Naser S., El- Hissi H., Abu- Rass M. and El- Khozondar N., "An Expert System for Endocrine Diagnosis and Treatments Using JESS", Journal of Artificial Intelligence, Vol. 3, No.4, 2010, pp.239-251.

[4] Bassem S., "Medical Expert Systems Survey". International Journal of Engineering and Information Systems (IJEAIS", Vol. 1, No.7, 2017, pp. 218-224.

[5] De Kock E., "Decentralising the Codification of Rules in A Decision Support Expert Knowledge Base", M.Sc. Thesis. Faculty of Engineering. Built Environment and Information Technology, University of Pretoria, 2003.

[6] Jimmy S., Dinesh G. and Abhinav B., "Medical Expert Systems for Diagnosis of Various Diseases", International Journal of Computer Applications, Vol.93, No.7, 2014.

[7] World Health Organization, ICD-11 Beta Draft (Mortality and Morbidity Statistics) Geneva: World Health Organization,, last accessed on 2017/2/10.

[8] World Health Organization, Factsheet Hepatitis A,, last accessed 2017/7/5.

[9] World Health Organization. Factsheet. Typhoid,, 2018/1.

[10] World Health Organization. Factsheet. Malaria,, 2018/4/20.

[11] World Health Organization. Factsheet. Cholera,, 2018/2.

[12] Oluwagbemi, O., Adeoye E. and Fatumo S., "Building a computer-based expert system for malaria environmental diagnosis: An alternative malaria control strategy", Egypt. Comput. Sci. J., Vol. 33, No.1, 2009, pp.55-69.

[13] Fatumo S., Adetiba E. and Onolapo J., "Implementation of XpertMalTyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid", IOSR Journal of Computer Engineering (IOSR-JCE) Vol. 8, No. 5, 2013, pp.34–40.

[14] Sunday T., Oriyomi A., Ayooluwa A. and Ayodeji D., "A Rule Based Expert System for Diagnosis of Fever", International Journal of Advanced Research, Vol. 1, No.7, 2013, pp.343-348.

[15] Samuel O., Omisore M. and Ojokoh B., "A Web Based Decision Support System driven by Fuzzy Logic for the diagnosis of typhoid fever", Expert Systems with Applications, Vol. 40, 2013, pp.4164-417.

[16] Mailafiya I. and Isiaka F., "Expert system for diagnosis of hepatitis B", West African Journal of Industrial and Academic Research, Vol. 4, No.1, 2012, pp.52-61.

[17] Nana Y., "MMES: A Mobile Medical Expert System for Health Institutions in Ghana", International Journal of Science and Technology, Vol. 2, No. 6, 2012, pp.333-344.

[18] Peter D., David L., Brian H., Jeff W. and Joe H., "Expert System for Providing Interactive Assistance in Solving Problems Such As Health Care Management", US5517405 A, 1996.

[19] Klaus-Peter Adlassnig, "The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences of the University of Vienna Medical School". Elsevier. Artificial Intelligence in Medicine. 21(2001), pp.139-146.

[20] Crina G. and Ajith A., "Intelligent Systems: A Modern Approach", Springer-Verlag Berlin Heidelberg, 2011.

[21] Krishnatnoorthy C. and Rajeev S., "Artificial Intelligence and Expert Systems for Engineers", CRC Press LLC, 1996.

[22] William S. and James J., "Fuzzy Expert Systems and Fuzzy Reasoning", John Wiley & Sons, Inc., 2005.

[23] Rimpy N., "Medical Expert System - A Comprehensive Review", International Journal of Computer Applications, Vol.130, No.7, 2015, pp.44-50.

[24] Kohavi, R. and Provost F., "Glossary of terms", Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, Vol. 30, No.2–3, 1998, pp. 271-274.

[25] Chala D., Million M. and Debela T., "Developing a Knowledge-Based System for Diagnosis and Treatment of Malaria", Journal of Information & Knowledge Management, Vol.15, No.4, 2016, pp.108-112.

[26] Nabeel Z., Abdullah A. and Sufian M., "Cloud Computing and Big Data is there a Relation between the Two: A Study", International Journal of Applied Engineering Research, Vol. 12, No.17, 2017, pp. 6970-6982.

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