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IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

The Development Of Fuzzy Set Theory In The Field Of Health

[Full Text]



Irma Ayuwanti, Marsigit, Dwi Siswoyo



Fuzzy Set Theory, Field of Health.



Fuzzy set theory is one of the set theories in mathematics, which discusses the set that cannot be stated only in the values of 0 and 1. The development of the Fuzzy set theory grows more rapidly in various fields. Fuzzy set theory develops in various forms of application. The development of Fuzzy set theory including in the fields of education, economics, agriculture, engineering, social and health. This article is a review of the development of the application of the fuzzy set theory in the health field. In the field of health the fuzzy dream system has been developed as an expert system used for disease diagnosis. The results of reviews from various sources, the development of the theory of Fuzzy set in health including, as a tool for diagnosing liver disease, diabetes mellitus, dengue fever (DHF) and typhoid fever, cardiovascular disease, cord blood analysis, heart disease, thyroid disease, diseases teeth and mouth. The development of the Fuzzy set is very useful in the field of health as a fast and precise innovation.



[1] L. a. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, 1965, doi: 10.1016/S0019-9958(65)90241-X.
[2] E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Int. J. Man. Mach. Stud., vol. 7, no. 1, pp. 1–13, 1975, doi: 10.1016/S0020-7373(75)80002-2.
[3] Y. Bai and D. Wang, “Fundamentals of Fuzzy Logic Control – Fuzzy Sets , Fuzzy Rules and Defuzzifications,” Adv. Fuzzy Log. Technol. Ind. Appl., pp. 334–351, 2006, doi: 10.1007/978-1-84628-469-4_2.
[4] H. J. Zimmermann, “Fuzzy set theory,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 3. pp. 317–332, 2010, doi: 10.1002/wics.82.
[5] S. Adzic and O. Sedlak, “Economic modelling and theory of fuzzy sets appliccation in macroeconomic planning within the process of transition,” Yugosl. J. Oper. Res., vol. 8, no. mic, pp. 331–341, 1998.
[6] J. L. do Nascimento, C. Stephan, and E. D. Nunes, “Social scientists in public health: a fuzzy approach,” Cien. Saude Colet., vol. 20, no. 5, pp. 1583–1593, 2015, doi: 10.1590/1413-81232015205.12692014.
[7] N. Werro, “Fuzzy Set Theory This,” in Fuzzy Classification of Online Customers, 2015, pp. 7–27.
[8] R. Rojas, “Fuzzy Logic,” Neural Networks, pp. 289–310, 1996, doi: 10.1007/978-3-642-61068-4.
[9] I. K. Ayuningtiyas, F. Saptono, and T. Hidayat, “Sistem Pendukung Keputusan Penanganan Kesehatan Balita Menggunakan Penalaran Fuzzy Mamdani,” Semin. Nas. Apl. Teknol. Inf. 2007 (SNATI 2007), 2007.
[10] J. M. Garibaldi, J. A. Westgate, and E. C. Ifeachor, “The evaluation of an expert system for the analysis of umbilical cord blood,” Artif. Intell. Med., vol. 17, no. 2, pp. 109–130, 1999, doi: 10.1016/S0933-3657(99)00020-2.
[11] B. Ambara, D. Putra, and D. Rusjayanthi, “Fuzzy Expert System of Dental and Oral Disease with Certainty Factor,” vol. 14, no. 3, pp. 22–30, 2017.
[12] A. Keleş and A. Keleş, “ESTDD: Expert system for thyroid diseases diagnosis,” Expert Syst. Appl., vol. 34, no. 1, pp. 242–246, 2008, doi: 10.1016/j.eswa.2006.09.028.
[13] W. W. Melek, a. Sadeghian, H. Najjaran, and M. Hoorfar, “A neurofuzzy-based expert system for disease diagnosis,” 2005 IEEE Int. Conf. Syst. Man Cybern., vol. 4, no. 1, 2005, doi: 10.1109/ICSMC.2005.1571727.
[14] A. Adeli and M. Neshat, “A Fuzzy Expert System for Heart Disease Diagnosis,” Proc. Int. MultiConference Engineeers Comput. Sci., vol. I, pp. 1–6, 2010.
[15] F. Tsukamoto, I. Pendahuluan, and D. Tifoid, “Dengan Metode Fuzzy Tsukamoto ( Studi Kasus Puskesmas Pracimantoro I ),” pp. 17–24.
[16] J. A. Sanz, M. Galar, A. Jurio, A. Brugos, M. Pagola, and H. Bustince, “Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system,” Appl. Soft Comput., vol. 20, pp. 103–111, 2014, doi: 10.1016/j.asoc.2013.11.009.
[17] T. Nguyen, A. Khosravi, D. Creighton, and S. Nahavandi, “Expert Systems with Applications Classification of healthcare data using genetic fuzzy logic system and wavelets,” vol. 42, pp. 2184–2197, 2015.
[18] R. B. Lukmanto and E. Irwansyah, “The Early Detection of Diabetes Mellitus (DM) Using Fuzzy Hierarchical Model,” Procedia Comput. Sci., vol. 59, no. Iccsci, pp. 312–319, 2015, doi: 10.1016/j.procs.2015.07.571.
[19] F. Ekajaya, N. Hidayat, and M. T. Ananta, “Diagnosis Penyakit Hati Menggunakan Menggunakan Metode Fuzzy Tsukamoto Berbasis Android,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 2373–2381, 2017.
[20] A. Hashmi and M. Saleem, “Diagnosis Blood Test for Liver Disease using Fuzzy Logic,” Int. J. Sci. Basic Appl. Res., vol. 20, no. 1, pp. 151–183, 2015.
[21] S. l Satarkar and M. S. Ali, “Fuzzy Expert System for the Diagnosis of Common Liver Disease,” Int. Eng. J. Res. Dev., vol. 1, no. 1, pp. 2–7, 2015, doi: 10.1109/EE.1944.6440600.
[22] P. K. Anooj, “Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules,” J. King Saud Univ. - Comput. Inf. Sci., vol. 24, no. 1, pp. 27–40, 2012, doi: 10.1016/j.jksuci.2011.09.002.
[23] Fauzan Masykur, “Implementasi Sistem Pakar Diagnosis Penyakit Diabetes Mellitus Menggunakan Metode Fuzzy Logic Berbasis Web,” 2012.
[24] J. M. Garibaldi and E. C. Ifeachor, “The Development of a Fuzzy Expert System for the Analysis of Umbilical Cord Blood,” System, vol. 10, pp. 129–144, 1997.
[25] J. Kim, J. Lee, and Y. Lee, “Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree,” Healthc. Inform. Res., vol. 21, no. 3, pp. 167–174, 2015, doi: 10.4258/hir.2015.21.3.167.
[26] N. Allahverdi, S. Torun, and I. Saritas, “Design of a Fuzzy Expert System for Determination of Coronary Heart Disease Risk,” Int. Conf. Comput. Syst. Te chnologies - CompSysTech’07, pp. 1–8, 2007, doi: 10.1145/1330598.1330638.
[27] E. P. Ephzibah and V. Sundarapandian, “A Neuro Fuzzy Expert System for Heart Disease Diagnosis,” Comput. Sci. Eng. An Int. J., vol. 2, no. 1, pp. 17–23, 2012, doi: 10.5121/cseij.2012.2103.
[28] A. V. Senthil Kumar, “Diagnosis of heart disease using fuzzy resolution mechanism,” J. Artif. Intell., vol. 5, no. 1, pp. 47–55, 2012, doi: 10.3923/jai.2012.47.55.
[29] M. H. F. Zarandi, M. Zolnoori, M. Moin, and H. Heidarnejad, “A Fuzzy Rule-Based Expert System for Diagnosing Asthma,” Sci. Iran. Trans. E-Industrial Eng., vol. 17, no. 2, pp. 129–142, 2010.
[30] M. Thirugnanam, P. Kumar, S. Vignesh Srivatsan, and C. R. Nerlesh, “Improving, the prediction rate of diabetes diagnosis using fuzzy, neural network, case based (FNC) approach,” Procedia Eng., vol. 38, pp. 1709–1718, 2012, doi: 10.1016/j.proeng.2012.06.208.
[31] S. S. A. Naser and A. E. A. El-najjar, “Nausea and vomiting problems in infants and children expert system,” vol. 1, no. 2, pp. 61–67, 2016.
[32] K. A. Y. Galala, “An expert system for diagnosis of problems in reinforced concrete structures,” Int. J. Adv. Res. Comput. Commun. Engeneering, vol. 4, no. 7, pp. 458–462, 2015, doi: 10.17148/IJARCCE.2015.47104.
[33] P. Lialiou, D. Zikos, and J. Mantas, “Development and evaluation of an expert system for the diagnosis of child autism,” Stud. Health Technol. Inform., vol. 180, pp. 1185–1187, 2012, doi: 10.3233/978-1-61499-101-4-1185.
[34] M. J. A. Ghali, M. N. Mukhaimer, M. K. A. Yousef, and S. S. A. Naser, “Expert System for Problems of Teeth and Gums,” vol. 1, no. 4, pp. 198–206, 2017.
[35] M. M. Yusof, R. A. Aziz, and C. S. Fei, “The Development of Online Children Skin Diseases Diagnosis System,” Int. J. Inf. Educ. Technol., vol. 45, no. Icikm, pp. 231–234, 2013, doi: 10.7763/IJIET.2013.V3.270.
[36] S. Tinuke, Olande. Yetunde, “Dental Expert System,” Int. J. Appl. Inf. Syst., vol. 8, no. 2, pp. 1–15, 2015, doi: 10.5120/ijais14-451270.
[37] Suharjito, Diana, Yulyanto, and A. Nugroho, “Mobile Expert System Using Fuzzy Tsukamoto for Diagnosing Cattle Disease,” Procedia Comput. Sci., vol. 116, no. Iccsci, pp. 27–36, 2017, doi: 10.1016/j.procs.2017.10.005.
[38] D. Permatasari, I. N. Azizah, H. L. Hadiat, and A. M. Abadi, “Classification of toddler nutritional status using fuzzy inference system (FIS),” vol. 040007, p. 040007, 2017, doi: 10.1063/1.4995122.
[39] A. Suryanto, O. Paramita, and F. S. Pribadi, “The development of android – based children’s nutritional status monitoring system,” vol. 020058, p. 020058, 2017, doi: 10.1063/1.4976922.
[40] D. Kartika, R. L. Gema, and M. Pratiwi, “Expert Systems for Identifying Children ’ s Severe Malnutrition,” J. Comput. Sci. Inf. Technol., vol. 2, no. October, pp. 20–29, 2016.
[41] M. Hazman and A. M. Idrees, “A healthy nutrition expert system for children,” 2015 E-Health Bioeng. Conf. EHB 2015, pp. 1–4, 2016, doi: 10.1109/EHB.2015.7391367.
[42] Y. Wulandari, “Aplikasi Metode Mamdani Dalam Penentuan Gizi Dengan Indeks Masa Tubuh (Imt) Dengan Menggunakan Logika Fuzzy,” 2011.