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An Expert System Using Certainty Factor for Determining Insomnia Acupoint
https://doi.org/10.22146/ijccs.26328 Elizabeth Paskahlia Gunawan(1*), Retantyo Wardoyo(2)
(1) Computer Science Program, Bina Nusantara Institute of Creative Technology Malang
(2) Department of Electronics and Computer Science, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
In treating insomnia patients, acupuncturists who are not always in their clinics trust their patients to their assistants but because of their assistants limited knowledge, their assistants can not determine the right acupoints. Therefore, an application that able to store their knowledge about insomnia disease treatment is needed so that their assistants can handle the patients like they do.
In this research, an expert system application using certainty factor method to determine the acupoint in dealing with insomnia disease was built. This research used certainty factor to accommodate uncertainty about symptoms and rules. The mechanism of certainty factor on symptoms used a measure of increased belief (MB) and a measure of increased disbelief (MD).
The built expert system resulted acupoints based on symptoms experienced by insomnia patients. Accuracy value produced by the system that used certainty factor for determining acupoint dealing with insomnia is 0.933. It showed that the acupoint produced by the system is 93.3% relevant according acupuncturist expertise in treating insomnia patients.
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[1] E. Mai and D. J. Buysse, “Insomnia: Prevalence, Impact, Pathogenesis, Differential Diagnosis, and Evaluation,” Sleep Med. Clin., vol. 3, no. 2, pp. 167–174, 2008.
[2] V. Erden, S. Abitağaoğlu, C. Güler, Z. Doğan, Ş. Kırgezen, and Y. Abut, “Insomnia may increase anesthetic requirement,” J. Clin. Anesth., vol. 34, pp. 367–372, Nov. 2016.
[3] K. L. Lichstein, D. J. Taylor, C. S. McCrae, and M. E. Petrov, “Chapter 81 – Insomnia: Epidemiology and Risk Factors,” in Principles and Practice of Sleep Medicine, 6th ed., M. Kryger, T. Roth, and W. C. Dement, Eds. China: Elsevier, 2017, pp. 761–768.
[4] Y. Ma, M. Dong, C. Mita, S. Sun, C.-K. Peng, and A. C. Yang, “Publication analysis on insomnia: how much has been done in the past two decades?,” Sleep Med., vol. 16, no. 7, pp. 820–826, Jul. 2015.
[5] A. Hashembeigi, R. Jarfaee, N. esmaeili Ahangarkola, and N. Rostamian, “The influence of acupuncture on sleep disorders and anxiety,” World J. Acupunct. - Moxibustion, vol. 25, no. 2, pp. 28–33, Jun. 2015.
[6] L. L. Chen and T. O. Cheng, Eds., Acupuncture in Modern Medicine. Croatia: InTech, 2013.
[7] J. Giarratano and G. Riley, Expert Systems: Principles and Programming, 4th ed. Boston: Course Techology, 2005.
[8] N. Hasanah and R. Wardoyo, “Purwarupa Sistem Pakar dengan Mamdani Product untuk Menentukan Menu Harian Penderita DM,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 7, no. 1, pp. 45–54, 2013.
[9] E. G. Wahyuni and W. Prijodiprojo, “Prototype Sistem Pakar untuk Mendeteksi Tingkat Resiko Penyakit Jantung Koroner dengan Metode Dempster- Shafer (Studi Kasus: RS. PKU Muhammadiyah Yogyakarta),” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 7, no. 2, pp. 133–144, 2013.
[10] R. S. C. M. Road and W. Bengal, “A Simple and Effective Expert System for Schizophrenia Detection,” Int. J. Intell. Syst. Technol. Appl., vol. 14, no. 1, pp. 27–49, 2015.
[11] Minarni and A. Fadhillah, “Expert System in Detecting Rice Plant Diseases,” J. Dyn., vol. 2, no. 1, pp. 11–15, 2017.
[12] A. P. Eka Setyarini, Darma Putra, “The Analysis of Comparison of Expert System of Diagnosing Dog Disease by Certainty Factor Method and Dempster-Shafer Method,” Int. J. Comput. Sci. Issues, vol. 10, no. 1, pp. 576–584, 2013.
[13] Hustinawaty and R. Aprianggi, “The Development of Web Based Expert System for Diagnosing Children Diseases Using PHP and MySQL,” Int. J. Comput. Trends Technol., vol. 10, no. 4, pp. 197–202, 2014.
[14] A. Alexiou, M. Psiha, and P. Vlamos, “Towards an Expert System for Accurate Diagnosis and Progress Monitoring of Parkinson’s disease,” in GeNeDis 2014 Neurodegeneration, 2015, vol. 820, no. November, pp. 151–164.
[15] J. Lever, M. Krzywinski, and N. Altman, “Points of Significance: Classification evaluation,” Nat. Methods, vol. 13, no. 8, pp. 603–604, 2016.
DOI: https://doi.org/10.22146/ijccs.26328
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