• Serpil SEVİM Istanbul Aydın University, Istanbul, Turkey



Diabetes, Artificial Intelligence, Data Mining


Diabetes is a chronic disorder caused by the inability of the pancreas to provide adequate insulin or to insure the body is not consumable. The height of the headingexperses of diabetes, which is caused by serious complications. After a certain time, there are serious complications such as eye diseases, cardiovascular diseases, kidney diseases, diabetes, the height of remediation experses and the person who is uncomfortable with the cause of loss of labour, socioeconomic is an important health problem forthecreation of boredom. Diabetes is often manifested by the average age in our ages and in adults. And because of these conditions, early diagnosis is important in diabetes as well as in many diseases. Different chemical tests are performed in blood and urine for diagnosis. In this study, the clinical data of individuals were examined and the data mining techniques were determined to determine whether individuals were diabetic.


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Arı, Önder, H. (2013), 'Regression Methods That Can Be Used in Different Data Structures', Anadolu Tarım Bilim Dergisi, 2013, 28 (3): 168-174, doi: 10.7161 / anajas.2013.28.3.168.

Aydemir, B. (2017), 'Academic Success Estimation of Vocational High School Students Using Data Mining Methods', T.C. Pamukkale University, Institute of Science, Computer Engineering Department, Denizli.

Aydın, F. (2011), “Development of a Machine Learning Based System to Support the Treatment Processes of Patients with Heart Rhythm Disorder”, T.C. Trakya University, Institute of Science, Edirne.

Çataloluk, H. (2012), 'Diagnosis of Disease Using Data Mining Methods on Real Medical Data', Bilecik University, Institute of Science, Computer Engineering Department, Bilecik.

Daoudy, A.E. and Maalmi, K. (2019), ‘A New Internet of Things Architecture for Real-Time Prediction of Various Diseases Using Machine Learning on Big Data Environment’, Journal of bigdata 6, article number: 104. DOI:

Erdost, Ş.K. and Çetinkale, O. (2008), 'Wound Careand Treatment', Istanbul University Cerrahpaşa Medical Faculty Continuing Medical Education Activities Symposium Series No: 67.

Fang, X. (2017), 'Understanding Via Back tracking and deconvolution', Journal of Big Data 4, Article Number: 40. DOI:

Gedik, S. (2016), 'Levels of Self-Effectiveness in Disease Management of Individuals with Type 2 Diabetes Living in Rural Areas', Selcuk University, Konya.

Strong, M.B., Sağlam, M.,İnce, D.İ., Savcı, S. and Arıkan, H. (2008), 'Diabetes and Exercise', Hacettepe University-Faculty of Health Sciences, Department of Physical Therapy and Rehabilitation, Ankara.

Graziano, DIICAN and Giuseppe, Seghier (2007), 'Normal Glucose Tolerance and Gestational Diabetes Mellitus', Diabetes Care 30: 1783-1788, 2007. DOI:

Kalaycı, T.E. (2006), “Creating and Implementing an Infrastructure with “Extensible3D” (X3D) for Three-Dimensional Graphics Software Using Artificial Intelligence Techniques”, Ege University Institute of Science.

Kamble, T P. (2016), 'Diabetes Detection Using Deep Learning Approach', International Journal for Innovative Research in Science & Technology Volume 2 Issue 12.

Koyuncugil, A.S. and Özgülbaş, N. (2009), 'Data Mining: Its Use and Applications in Medicine and Health Services', Journal of Information Technologies, Volume: 2, Issue: 2.

Köse, U., Güraksin, G.E. and Deperlioğlu, Ö. (2015), 'Diabetes Detection with Vortex Optimization Algorithm Based Support Vector Machines', Medical Tekno'15 Medical Technologies National Congress. DOI:

Losiewicz, P., Oard, D.W. and Kostoff, R.N. (2000), 'Textual Data Mining to Support Science and Technology Management', Journal of Intelligent Information Systems, 15, 99-119, 2000. DOI:

Nacafabadi, M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and Muharemagic, E. (2015), 'Deep Learning Applications and Challenges in Big Data Analytics', Journal of Big Data 2, Article Number: 1. DOI:

Oksayşah, A. (2015), 'skin in individuals with diabetes, Dental Care', Turkey Clinics, internmednurs-Special Topics in 2015: 1 (3), Compilation Review.

Pehlivan, R. (2014), 'Classification in Painting Based Ottoman Documents', T.C. İstanbul Kültür University Institute of Science.

Samancıoğlu, S. (2013), 'Preclinical Study for Diabetic Foot Care: Comparison of Classical Wound Dressing Material and Olive Leaf Extract in Ischemic Wound Care Created in Rats with Experimental Diabetes Model', T.C Ege University Institute of Health Sciences, Research Gate.İzmir.

Sivri, E.Ş. (2015), 'Data Mining / Development of a Product Advice System for E-Commerce', T.C. İstanbul TicaretÜniversitesi. Istanbul.

Şanlı, E. (2018), 'Artificial Neural Network Controlled Autonomous Rc Vehicle Application', T.C. İstanbul Gelişim University Institute of Science. Istanbul.

Şata, M. (2015), "Investigation of High School Students' Attitudes towards Physics Course Comparatively with Chaid Analysis and Logistic Regression Analysis", Gazi University Institute of Educational Sciences.

T.R Ministry of National Education, (2011), 'First Aid in Other Emergency Situations 720S00043', Ankara.

Talaz, A. (2007), “Evaluation of Blood Sugar Control and Psychosocial Compliance in Patients with and Without Diabetic Foot”, T.C Marmara University Institute of Health Sciences. Istanbul.

Ting, D.Ş.W., Yim, C., Cheung, L., and Lim, G. (2017), 'Development and Validation of a Deer Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic Populations with Diabetes', Jama. Doi: 10.1001.

Thomas A. Buchanan, MD1, Anny Xiang, PHD2, Siri L. Kjos, MD3 and Richard Watanabe, PHD2 (2007), 'what is Gestational Diabetes?', Diabetes Care 2007 Jul; 30 (Supplement 2): S105-S111. DOI:

Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang and Joel T. Dudley (2018), 'Deep learning for health care: review, opportunities and challenges', Oxford Journals Briefings in Bioinformatics.

Ruben D. Canlas Jr, MSİT, MBA (2009), 'Data Mining in Healthcare: Current Applications and Issues', Paper submitted to fulfill requirements for the Master of Science in Information Technology at the Carnegie Mellon University Australia.

Yanık, Y.T. (2011), 'Assessment of Self-Efficacy Levels of Type II Diabetics', T.C. Trakya University, Institute of Health Sciences, Edirne.




How to Cite

SEVİM, S. (2020). PRE-DETECTION OF DISEASES WITH DEEP LEARNING METHOD AND AN APPLICATION ON DIABETES. International Journal of Engineering Technologies and Management Research, 7(2), 143–155.