[1] Asrardel, M. (2015). “Prediction of combustion dynamics in an experimental turbulent swirl stabilized combustor with secondary fuel injection”, MSc Thesis, University of Tehran.
[2] Bagherzadeh Khiabani, F., Akhavan Niaki, S.T. (2013). “An ensemble model for prediction of diabetes type 2”, The Seventh Iran Data Mining Conference, Tehran, Iran.
[3] Barakat, N.H., Bradley, A. P., Barakat, M.N.H. (2010). “Intelligible support vector machines for diagnosis of diabetes mellitus”. IEEE Transactions on Information Technology in Biomedicine, 14(4).
[4] Cameron, A.J., Shaw, J.E., Zimmet, P.Z. (2004). “The metabolic syndrome: Prevalence in worldwide populations”, Endocrinol Metab Clin, 33, 351-357.
[5] Deepa, M., Farooq, S., Datta, M., Deepa, R., Mohan, V. (2007). “Prevalence of metabolic syndrome using WHO, ATPIII and IDF definitions in Asian Indians”, the Chennai Urban Rural Epidemiology Study, 23, 127-134.
[6] Dehghandar, M., Ahmadi, G., Aghebatbeen Monfared, H. (2021). “Designing a fuzzy expert system for diagnosis and prediction of metabolic syndrome in children and adolescents”, Health Management & Information Science, 21.
[7] Dehghandar, M., Hassani Bafarani, A., Dadkhah, M., Ghorbani, M., Klishadi, R. (2021). “Diagnosis and prediction of obesity and hypertension in Isfahanian students using artificial neural network”, Health and Biomedical Informatics, 8(5).
[8] Dehghandar, M., Pabasteh, M., Heidari, R. (2021). “Diagnosis of Covid-19 disease by fuzzy expert system designed based on input-output”, Journal of Control, 14(5), 71-78.
[9] Ebrahimpour, P., Fakhrzadeh, H., Poorabrahim, R., Hamidi, A., Heshmat, R., Nouri, M., Larijani B. (2017). “Metabolic syndrome and its relationship with insulin levels in obese primary school children in the sixth district of Tehran”, Razi Journal of Medical Sciences, 13(51), 7-16.
[10] Kaplan, N.M. (1989). “The deadly quartet upper body obesity, glucose intolerance, Hypertrigly ceridemia and Hypertension”, Arch Intern 1989; 149, 1514-1520.
[11] Karimian, Z., Kojouri, J., Sagheb, M. (2015). “A review of the evidence-based medicine realm based on two factors”, The Nature of Science and Decision Making Situation, 15.
[12] Khosravanian, A., Ayat, S. (1997). “An intelligent medical system based on artificial neural network in diagnosis of diabetes”, Diabetes and Metabolism, 18(2), 71-79.
[13] Khosravi, M., Zarei, H., Kheiri Donighi, S. (2018). “Diabetes based on fuzzy systems and Wall optimization algorithm”, New Ideas in Science, Engineering and Technology, 2(2), 62-75.
[14] Klishadi, R. (2012). “Comparison of the effect of vitamin D and placebo on the components of metabolic syndrome in children and adolescents aged 10-16 years”, Pediatric Developmental Research Center of Isfahan University of Medical Sciences. Jornal de Pediatria, 90(1), 28-34.
[15] Korenevskiy, N.A. (2015). “Application of fuzzy logic for decision-making in medical expert
systems”, Biomedical Engineering, 49, 46-49.
[16] Langarizadeh, M., Khajehpour, E., Khajehpour, H., Nouri, T. (2014). “Fuzzy expert system for distinguishing bacterial meningitis from other meningitis in children”, Health and Biomedicine Informatics, 1, 19-25.
[17] Nizami F., Farooqui, M.S., Munir, S.M., Rizvi, T.J. (2004). “Effect of fiber bread on the management of diabetes mellitus”, Coll Physicians Surg Pak, 14, 673-676.
[18] Saeedi, M. (2017). “Metabolic syndrome and hypertension in diabetic patients”, Endocrinology Metabolism, 11(1), 11-16.
[19] Salari, M., Kiwani, O., Soleimani, A. (2013). “Designing a fuzzy expert system to diagnose the risk of hypertension” Civilica, ICEEE05, 347-352.
[20] Sedehi, M., Mehrabi, Y., Kazemnejad, A., Hadayegh, F. (2013). “Comparison of artificial neural network models with logistic regression and audit analysis in predicting metabolic syndrome”, Endocrinology and Metabolism, 11(6), 638-646.
[21] Grundy, S.M. (2008). “Metabolic syndrome pandemic”, Originally Published, 28, 629-636.
[22] Mehrdad, M., Hossein Panah, F., Azizi, F. (2016). “Prevalence of metabolic syndrome among 3-9 years old children inTehran Lipid and Glucose Study”, Medical Research, 30(4), 337-346.
[23] Sharghi, S. (2014). “Metabolic syndrome and obesity”, Diabetes and Metabolism; 12(5), 399-412.
[24] Sheikh Taheri, A., Hamedan, F., Sandgol, H., Orouji, A. (2017). “Establishment of a fuzzy expert system for diagnosing chronic kidney disease”, Razi Journal of Medical Sciences, 25(10).
[25] Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., Johannes, R.S. (1998). “Using the ADAP learning algorithm to forecast the onset of diabetes mellitus”, In Proceedings of 12th Symposium on Computer Applications in Medical, Care, R. A. Greenes, Ed. IEEE Computer Society Press, 261-265.
[26] Su, C.T., Yang, C.H., Hsu, K.H., Chiu, W.K. (2006). “Data mining for the diagnosis of type II diabetes from three dimensional body surface anthropometrical scanning data”, Computers and Mathematics with Applications, 1075-1092.
[27] Li-Xin W. (1996). “A course in fuzzy systems and control”, Prentice Hall International, Inc.
[28] Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Pidetcha, P., Prachayasittikul, V. (2010). “Identification of metabolic syndrome using decision tree analysis”, diabetes research and clinical practice, 90, 15-18.
[29] Yousefi Zanouz, R., Elmaei, R., Elmaei, S. (2018). “Provide a fuzzy expert system to model
the diagnosis of coronary heart disease”, Industrial Management Studies, 50.
[30] Zabah, I., Eskandari, A., Sardari, Z., Noghanzi, A. (2016). “Diagnosis of diabetes using artificial and neural-fuzzy neural network”, Torbat Heydariyeh University of Medical Sciences, 6(2).
[31] Zarkesh, M., Daneshpour, M.S., Faam, B., Fallah, M.S., Hosseinzadeh, N., Guity, K., Hosseinpanah, F., Momenan, A.A., Azizi, F. (2012). “Heritability of the metabolic syndrome and its components in Tehran lipid and glucose study (TLGS)”, Genet Res (Camb), 94(6), 331-337.
[32] Zimmet, P., Alberti, G., Kaufman, F., Tajima, N., Silink, M., Arslanian, S. (2007). “The metabolic syndrome in children and adolescents”, International Diabetes Federation Task Force on Epidemiology and Prevention of Diabetes, 8(5), 299-306.
[33] Sandoval, J.A., Lucero, J., Oetzel, J., Avila, M., Belone, L., Mau, M., Pearson, C., Tafoya, G., Duran, B., Iglesias Rios, L., Wallerstein, N. (2012). “Process and outcome constructs for evaluating community-based participatory research projects: a matrix of existing measures”. Health Education Research, 27(4), 680-690.