ادهم، داود؛ مهدوی؛ عبدالله؛ مهرتک، محمد؛ ابراهیمی، کمال؛ آذری، آرزو (1394 ). مقایسه تخصیص منابع انسانی بیمارستانهای عمومی دانشگاهی شهرستانهای استان آذربایجان شرقی با استاندارد کشوری. مجله سلامت و بهداشت، 6(5)، 507- 516.
تقیزاده یزدی، محمد رضا؛ میرشجاعیان حسینی، حسین؛ اصغری زاده، عزتاله. .؛ شکوری گنجوی، حامد (1394). مقایسه عملکرد روشهای رگرسیون آماری و فازی در تخمین تابع تقاضای بنزین (مطالعه موردی در ایران). مدیریت صنعتی، 7(1)، 1-19.
شریعت پناهی، سید پیام؛ حبیبی، دانیال؛ رفیعی ، محمد؛ قندی، یزدان؛ انوری، مهدی (1396 ). تعیین ارتباط بین فشار خون سیستولیک و سکته قلبی در بیماران دیابت نوع دو با استفاده از قوانین انجمنی. مجلهعلمیپژوهشیدانشگاهعلومپزشکیاراک، (12)20، 44-50.
عباسی رنجبر، زهرا (1384). شیوع علائم بیماری آسم در کودکان. مجله علوم پزشکی دانشگاه گیلان، 14(56)، 1-9.
عصاری، راحله؛ مدرسی، محمدرضا؛ حقجوی، شقایق؛ لاهیجان زاده، احمدرضا (1389). بررسی ارتباط سطح آلودگی هوا با سطح سرمی ترومبومودلین و فاکتور بافتی در نوجوانان منتخب 10-18 ساله اصفهان. مجله دانشکده پزشکی اصفهان، 28 (109)، 425-436.
فرجزاده، منوچهر؛ حیدری، آزیتا (1391). تحلیل اثر اقلیم بر بیماریهای عفونی کودکان در منطقه بندرعباس. مجله پژوهشی حکیم، 16(1)، 72-79.
کریمیپور، فرید؛ کنعانی سادات، یوسف (1393). بررسی وابستگی وقوع بیماری آسم آلرژیک با ویژگیهای محیطی با استفاده از کاوش قوانین وابستگی مکانی فازی. نشریه علمی ـ پژوهشی علوم و فنون نقشهبرداری، 4(3)، 117-129.
محمدی، جیران؛ آذر، عادل؛ زارعی متین، حسن (1384). طراحی مدل برنامهریزی نیروی انسانی برای بیمارستانهای آموزشی مورد مطالعه: بیمارستانهای آموزشی شهر اهواز. فصلنامه علمی ـ پژوهشی دانشگاه شاهد، 12(11)، 79- 90.
نشاط، نجمه؛ محلوجی، هاشم (1388). کنترل پیشبینانه کیفیت با استفاده از شبکههای عصبی مصنوعی (ANNs) و روش ترکیبی تحلیل رگرسیون و ANNs. مدیریت صنعتی، 1(2)، 153-170.
References
Abbasi Ranjbar, Z. (2006). Prevalence of Asthma Symptoms in Children. Jounal ofr guilan university medical science, 14 (56), 1-9. (in Persian)
Adham, D., Mahdavi, A., Mehrtak, M., Ebrahimi, K., & Azari, A. (2016). Assessment of Human Resource Allocation in General University Hospitals in the Cities of East Azerbaijan Province. Journal of health, 6 (5), 507-516. (in Persian)
AL-Hamodi, A., Songfeng, L., & AL-Salhi, Y. (2016). An enhanced frequent pattern growth based on mapreduce for mining association rules. International Journal of Data Mining & Knowledge Management Process (IJDKP), 6(2), 19-28.
Alwidian, J., Bassam, H., & Obeid, N. (2018). WCBA: Weighted classification based on association rules algorithm for breast cancer disease. Applied Soft Computing, 62, 536–549.
Assari, R., Modarresi, M., Haghjou Javanmard, SH., Lahijanzadeh, A., Poursafa, P., Sadeghian, B., & Kelishadi, R. (2010). Evaluation of relationship between air pollution level and serum thrombomudolin and tissue factor in selected sample from 10 to 18 years old adolescents in Isfahan. Journal of Isfahan medical science, 28 (109), 425-436.
(in Persian)
Bhatt, U., & Pratik, P. (2014). A Recent Overview: Rare Association Rule Mining. International Journal of Computer Applications, 107(8), 1-4.
Borah, A., & Nath, B. (2018). Identifying Risk Factors for Adverse Diseases using Dynamic Rare Association Rule Mining. Expert systems with applications, 113, 1-62.
Cano, A., Zafra, A., & Ventura, S. (2013). An interpretable classification rule mining algorithm. Information Sciences, 240, 1-20.
D’souza, R. M., Hall, G., & Becker, N. G. (2007). Climatic factors associated with hospitalizations for rotavirus diarrhoea in children under 5 years of age. Epidemiol, 136, 56-64.
Dash, M., & Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1), 155-176.
Dougherty, J., Kohavi, V., & Sahami, M. (1995). Supervised and Unsupervised Discretization of Continuous Features. Proceedings of the Twelfth International Conference on Machine Learning. Tahoe City, California, USA.
Farajzadeh, M., & Hydari, A. (2013). A. Relationship between Climate changes with Children's Infectious Diseases in Bandar Abbas, Iran. Hakim health system research journal, 16 (1), 72-79. (in Persian)
Hadi, W., Aburub, F., & Alhawari., S. (2016). A new fast associative classification algorithm for detecting phishing websites. Applied Soft Computing, 48, 729–734.
HervásaL, A., & Marcos, G. (2015). Can meteorological factors forecast asthma exacerbation in a paediatric population? Allergologia et Immunopathologia, 43 (1), 32-36.
Hu, L., Hu, Y., Tsai, C., Wang, J., & Huang, M. (2016). Building an associative classifier with multiple minimum supports. Speringer plus, 5, 1-19.
Ivančević, V., Tǔsek, I., Jasmina, J., Kneˇzevi´, M., Elheshk, S., & Lukovi, I. (2015). Using association rule mining to identify riskfactors for early childhood caries. Computer Methods and Programs in Biomedicine, 122(2), 175-181.
Javed, K., Babri, A., & Saeed, M. (2012). Feature Selection Based on Class-Dependent Densities for High-Dimensional Binary Data. IEEE Transactions on Knowledge and Data Engineering, 24(3), 465-477.
Karimipour, F., & Kananisadat, Y. (2015). Investigating the Relation between Prevalence of Asthmatic Allergy with the Characteristics of the Environment Using Fuzzy Association Rule Mining. Journal of Geomatics Science and Technology, 4 (3), 117-130. (in Persian)
Kim, SH., Kim, JS., Jin, MH., Jin, C., & Lee, JH. (2017).The effects of weather on pediatric seizure: A single-center retrospectivestudy (2005–2015). life-sciences literature, 609, 535-540.
Koh, Y. & Nathan, R. (2010). Rare Association Rule Mining and Knowledge Discovery: Technologies for infrequent and critical event detection , New Zland, Information Science Reffrence.
Liu, B., Hsu, W., & Ma, Y. (1998). Integrating Classification and Association Rule Mining, KDD’98 Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, USA.
Martína, MR. & Bayleb, MS. (2018). Impact of air pollution in paediatric consultations in Primary Health Care. Ecological study, Pollution and paediatric consultations in Primary Health Care, 89 (2), 80-85.
Mireku, N., Wang, Y., Ager, J., Reddy, R.C., & Baptist, AP. (2009). Changes in weather and the effects on pediatric asthma exacerbations. Annals of Allergy, Asthma & Immunology, 103: 221-224.
Mohammadi, J., Azar, A., & Zareei Matin, H. (2005). Designing a Model of Manpower Planning for Educational Hospitals in Ahwaz. Daneshvar Raftar, 12 (11), 79-90. . (in Persian)
Nahar , J., Tasadduq, I., Tickle, k., Yi-Ping Phoebe, Ch. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40, 1086–1093.
Neshat, N., & Mahlooji, H. (2010). Predictive Process Control Using Artificial Neural Networks (ANNs) and A Combined Method of Regression Analysis and ANNs. Journal of Industrial Management, 1 (2), 153-170. (in Persian)
North, M. (2012). Data Mining for the Masses. Washington, Global Text Project.
Onozuka, D., & Hashizume, M. (2011). The influence of temperature and humidity on the incidence of hand, foot, and mouth disease in Japan Science of the Total Environment. Science of the Total Environment, 410, 119-125.
Ordonez, C. (2006). Association Rule Discovery With the Train and Test Approach for Heart Disease Prediction. IEEE Transactions on Information Technology in Biomedicine, 10(2), 334- 343.
Ordonez, C., Omiecinski, E., de Braal, L., Santana, C., Ezquerra, N., Taboada, J., Cooke, D., rawczynska, E., & Garcia, E. (2001). Mining Constrained Association Rules to Predict Heart Disease. Proceedings 2001 IEEE International Conference on Data Mining. San Jose, USA.
Rahal, I., Dongmei, R., Weihua, W., & Perrizo, W. (2004). Mining Confident Minimal Rules with Fixed-Consequents. In Proceedings OF THE 16TH IEEE International Conference ON Tools With Artificial Intelligence. IEEE Computer Society. Washington.
Reif, M., & Shafait, F. (2014). Efficient feature size reduction via predictive forward selection. Pattern Recognition, 47(4):1664-1673.
Shariatpanahi, S P., Habibi, D., Rafiei, M., Ghandi, Y., & Anvari, M. (2018). Determination of Relations between Systolic Blood Pressure and Heart Attack in Patients with Type 2 Diabetes with Association Rules. Journal of Arak University Medicla Science, 20 (12), 44-50. (in Persian)
Sridevi, R., & Ramaraj, E. (2013). A General Survey on Multidimensional And Quantitative Association Rule Mining Algorithms. International Journal of Engineering Research and Applications, 3(4), 1442-1448.
Taghizadeh Yazdi, M., Mirshojaeian Hosseini, H., Asgharizadeh, E., & SHakouri Ganjavi, H. (2015). Relative Performance of Statistical and Fuzzy Regression Models in Estimation of Gasoline Demand in Iran. Journal of Industrial Management, 7 (1), 1-19. (in Persian)
Tauler, E., Llorens-Terol, J., Mur, A., & Leal, C. (1985). Asthma and Environmental Factors. Pediatric Research 19, 1120-1129.
Thabtah, F. )2007(. A review of associative classification mining. The Knowledge Engineering Review, 22(1), 37-65.
Toti, G., Vilalta, V., Lindner, P., Lefer, B., Macias, C., & Price, D. (2016). Analysis of correlation between pediatric asthma exacerbation andexposure to pollutant mixtures with association rule mining. Artificial Intelligence in Medicine, 74, 44-52.
Wedyan, S. )2014(. Review and Comparison of Associative Classification Data Mining Approaches. International Journal of Industrial and Manufacturing Engineering, 8(1), 34-45.
Weng, Ch. )2011(. Mining fuzzy specific rare itemsets for education data. Knowledge-Based Systems, 24, 697-708.
Weng, Ch.) 2016(. Identifying association rules of specific later-marketed products, Applied Soft Computing, 38,528-529.
Zahng, Ch. & Zhang, SH. (2007). AssociationRule Mining Models and Algorithms. Springer-Verlag Berlin Heidelberg NewYork.
Zucco, Ch. ) 2019(. Data Mining in Bioinformatics. Reference Module in Life Sciences, 1, 328-335.