The development of a rule-based system for the diagnosis of candidiasis

Authors

  • Idongesit Efaemiode E Department of Computer Science, University of Calabar, Calabar, Cross River State, Nigeria
  • Udeze LC Department of Computer Science, University of Calabar, Calabar, Cross River State, Nigeria
  • Anuonye C Department of Computer Science, University of Calabar, Calabar, Cross River State, Nigeria
  • Okon U Department of Computer Science, University of Calabar, Calabar, Cross River State, Nigeria

Keywords:

Candidiasis, Rule-based system, Expert system, Diagnosis

Abstract

Candidiasis is one of the most common opportunistic fungal infections in existence. Globally, about 75% of women suffer from at least one episode of vulvovaginal candidiasis (VVC) annually. The nature of genital candidiasis infections, which includes vaginal and balanitis (inflammation of the glans penis) often hinders patients from seeking appropriate medical assistance in formal health facilities because these patients usually feel shy to engage experts. We developed a Web-based Expert System that provides a self-administered diagnosis of this condition. The system provides a diagnosis in conjunction with laboratory tests verification before treatment advice and remedial measures are recommended. Requirement gathering and specification were carried out through informal interviews and interactive sessions with health care providers, visitation to the laboratory and review of relevant literature on candidiasis infections. Simple production rules, as well as coded questions and answers, were employed for inferences. Valid and reliable inferences were made regardless of the Candidiasis scenario cases used in testing the system. Fifty (50) persons were tested and an average of 12% tested positive to the five (5) various types of Candidiasis. Treatment was recommended for such patients. The system could serve as initial diagnosis support for candidiasis.

References

. OPATHY Consortium and Gabaldon, T. 2019. Recent trends in the molecular diagnostics of yeast infections: from PCR to NGS. FEMS Microbiology Reviews, fuz015, 43, 517-547.

. Eggimann, P., Garbino, J. and Pittet, D. 2003. Epidemiology of Candida species infections in critically ill non-immunosuppressed Candida species infections in critically ill non-immunosuppressed. Lancet Infect Dis 3 , 685–702.

. Pappas, P. G., Kauffman, C. A., Andes, D. R., Clancy, C. J., Marr, K. A., Ostrosky-Zeichner, L. et al. 2016. Clinical Practice Guideline for the Management of Candidiasis: 2016 Update by the Infectious Diseases Society of America. Clinical Infectious Diseases 62(4), e1-e50.

. Martins, N., Ferreira, I. C. F. R., Barros, L., Silva, S and Henriques, M. 2014. Candidiasis: Predisposing factors, prevention, diagnosis and alternative treatment. Mycopathologia.

. Hayes-Roth, F. 1984. The Knowledge-based Expert System. IEEE Computer , 11-28.

. Davis, R. 1986. Knowledge-based systems. Science, 231, 957-964.

. O'Leary, D. E. 1996. History, Structure, Definitions, Characteristics, Life Cycle and Applications. Expert Systems , 1-2.

. Zainab T. D. and Abbas A-B. 2019. Medical Diagnosis Advisor System: A Survey. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 8(1).

. Imhanlahimi R. E. and John-Otumu A. M. 2019. Application of Expert System for Diagnosing Medical Conditions: A methodological review. European Journal of Computer Science and Information Technology, 7(2): 12-25.

. Soltan, R. A., Rashad, M. Z., and El-Desouky, B. 2013. Diagnosis of Some Diseases in Medicine via Computerized Experts System, International Journal of Computer Science and Information Technology, 5(5): 79 – 90.

. Fatumo, S.A., Adetiba, E., and Onaolapo, J.O. 2013. Implementation of XpertMalTyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid, IOSR Journal of Computer Engineering, 8(5): 34 – 40.

. Tunmibi, S., Adeniji, O., Aregbesola, A., and Ayodeji, D. 2013. A Rule Based Expert System for Diagnosis of Fever, International Journal of Advanced Research, 1(7): 343 – 348.

. Hambali, M. A., Akinyemi, A. A., and Luka, J. D. 2017. Expert System For Lassa Fever Diagnosis Using Rule Based Approach, Annals Computer Science Series, 15(2): 68 – 74.

. Hossain, M. S., Khalid, M. S., Akter, S., and Dey, S. 2014. A belief rule-based expert system to diagnose influenza. In proceedings of Strategic Technology 9th International Forum 2014 IEEE.

. Bursuk, E., Ozkan, M., and Llerigelen, B. 1999. A medical expert system in cardiological diseases. In proceedings of Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society 1999 IEEE.

. Ibrahim, F., Ali, J. B., Jaais, A.F., and Taib, A. F. 2001. Expert system for early diagnosis of eye diseases infecting the Malaysian population. In proceedings of IEEE Region 10th International Conference on Electrical and Electronic Technology TENCON 2001 IEEE.

. Singla, J. 2013. The Diagnosis of Some Lung Diseases in a PROLOG Expert System. International Journal of Computer Applications, 78(15): 37 – 40.

. Komal, R. H., and Vijay, S. G. 2014. Rule-Based Expert System for the Diagnosis of Memory Loss Diseases, International Journal of Innovative Science, Engineering & Technology, 1(3).

. Patel, M., Patel, A., and Virparia, P. 2013. Rule Based Expert System for Viral Infection Diagnosis, International Journal of Advanced Research in Computer Science and Software Engineering, 3(5).

. Naser M., Yousef A. S., Alaa N. A., Abdelbaset A., Adel A., Ahmed Y. M., Ihab Z. et al. 2019. Survey of Rule-based Systems. International Journal of Academic Information Systems Research (IJAISR), 3(7): 1-22.

. Dabas, P. S. 2013. An approach to etiology, diagnosis and management of different types of Candidiasis. Feast and Fungal Research (Doi: 10.5897/JYFR2013.0113), 4(6): 63-74.

. Paul, A., Jolley, C. and Anthony, A. 2018. Reflecting the Past, Shaping the Future: Making AI Work for International Development. USAID.

https://www.usaid.gov/sites/default/files/documents/15396/AI-ML-in-Development.pdf in July, 2020.

. Caceres, D. H., Forsberg, K., Welsh, R. M., Sexton, D. J., Lockhart, S. R., Jackson, B. R. et al. 2019. Candida auris: A review of recommendations for detection and control in healthcare settings. Journal of Fungi, 5, 111 .

. Sommerville I. 2011. Software Engineering, 9th Edition. Pearson Education Inc., Boston, Massachussetts, U.S.A. pp. 30-33.

. Oluwagbemi, O., Adeoye, E., and Fatumo, S. 2009. Building a Computer-Based Expert System for Malaria Environmental Diagnosis: An Alternative Malaria Control Strategy. Egyptian Computer Science Journal, 33(1) .

. Patra, S. K., Sahu, D. P. and Mandal, I. 2010. An Expert System for Diagnosis of Human Diseases. International Journal of Computer Applications. 1(13).

. Eteng, I. E., Okoro, A., Olufemi, O. and Esu E. 2016. An Expert System for the Diagnosis of the Ebola Virus Disease (EVD).

. Helmy T. 2009. Principles of Artificial Intelligence. KFUPM Open Courseware. Lecture Notes. OpenCourseware Consortium. Retrieved from http://opencourseware.kfupm.edu.sa/colleges/ccse/ics/ics381/files/2_Lectures%2033-35- Expert%20Systems-Ch.16.pdf in June, 2020.

. Udeze, L. C., Umoren, P. U., Oheri, H. E. and Attah, H. H. 2017. Automated Students‟ Results Management Information Systems (SRMIS). Journal of Multidisciplinary Engineering Science and Technology, 4(10).

. Agbasonu, V.C. & Udoka, L. N. 2015. Design and Implementation of an Expert System for the diagnosis of Ebola disease in Africa. Med-e-Tel. Retrieved from http://www.medetel.lu/index.php?rub=educational_program&page=programin February, 2020.

. World Health Organization, 2020. Laboratory Testing for Coronavirus Disease 2019 (COVID-19) in suspected human cases. Retrieved from http://www.leoportals.com

Downloads

Published

2024-02-26

How to Cite

Idongesit Efaemiode, E., Udeze, L. C., Anuonye, C., & Okon, U. (2024). The development of a rule-based system for the diagnosis of candidiasis. COMPUSOFT: An International Journal of Advanced Computer Technology, 9(09), 3848–3855. Retrieved from https://ijact.in/index.php/j/article/view/591

Issue

Section

Original Research Article

Similar Articles

<< < 10 11 12 13 14 15 16 17 18 19 > >> 

You may also start an advanced similarity search for this article.