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Kronik Böbrek Hastalarının Yapay Sinir Ağı ve Radyal Temelli Fonksiyon Ağı ile Teşhisi

Year 2015, Volume: 6 Issue: 2, 82 - 88, 31.12.2015

Abstract

Teknolojideki gelişmelerin tıp alanına uygulanması ile sağlık çalışanlarına ve hastalara büyük kolaylıklar sağlamıştır. Sağlık birimlerinde hastaya ait kişisel bilgilerin yanında laboratuvar sonuçları, tanı, teşhis tedavi gibi bilgiler de elektronik olarak kaydedilmektedir. Bu veriler, modern tekniklerle analiz edilerek, hekim ve yöneticilerin hizmetine sunulmalıdır. Yapılan çalışmada, hastaların laboratuvar sonuçları ve kişisel bilgilerine göre kronik böbrek hastası olup olmadığı tespiti yapılmıştır. Bunun için 400 hastanın laboratuvar sonuçlarından elde edilen 24 niteliğe sahip veriler geri yayılımlı yapay sinir ağı (YSA) ve radyal temelli fonksiyon ağı (RBFN)  ile sınıflandırılarak karşılaştırılmıştır.  Sınıflandırma sonucunda YSA ve RBFN performans değerleri sırasıyla 0,0172 ve 1.47585e-27 elde edilmiştir. YSA ile RBFN karşılaştırıldığında ise RBFN daha iyi bir performans göstermiştir.

Anahtar Kelimeler: Kronik böbrek hastalığı, sınıflandırma, teşhis, RBFN, YSA

References

  • Ababaei, B., Sohrabi, T., Mirzaei, F. (2012). Assessment of radial basis and generalized regression neural networks in Daily reservoir inflow simulation. Elixir Comp. Sci. & Engg. 42: 6074-6077.
  • Abhishek, B., Thakur, G.S.M., Gupta, D. (2012). Proposing Efficient Neural Network Training Model for Kidney Stone Diagnosis. (IJCSIT) International Journal of Computer Science and Information Technologies, 3 (3):3900-3904.
  • Afif, M.H., Hedar, A-R., Hamid, T.H.A., Mahdy, Y.B. (2013). SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis. (IJACSA) International Journal of Advanced Computer Science and Applications, 4(2):53-58.
  • Anthony, M., Bartlett, P.L. (2009). Neural Network Learning: Theoretical Foundation. Cambridge University Press.
  • Arisariyawong, S. A., Charoenseang, S. (2002). Dynamic self-organized learning for optimizing the complexity growth of radial basis function neural networks. Industrial Technology. IEEE ICIT'02. IEEE International Conference.
  • Delican, Y. , Özyilmaz, L., Yıldırım, T. (2011). Evolutionary Algorithms Based RBF Neural Networks For Parkinson s Disease Diagnosis. ELECO 2011 7th International Conference on Electrical and Electronics Engineering, 1-4 December, Bursa, TURKEY.
  • Graupe, D. (2013). Principles of Artificial Neural Networks: 3rd Edition (Advanced Series in Circuits & Systems) (Advanced Series in Circuits and Systems) . World Scientific Publishing Company; 3rd edition edition.
  • İlkuçar, M., Işık, A.H., Çifci, A. (2014). Classification of Breast Cancer Data with Harmony Search and Back Propagation Based Artificial Neural Network. IEEE, 2014 22 nd Signal Processing and Communications Applications Conference (SIU), 23-25 April 2014.
  • Jang, J-S.R., Sun, C.-T., Mizutani, E.,(1997), Neuro-Fuzzy and Soft Computing: A Computational Approach to Learningand Machine Intelligence. Prentice Hall; 1 edition (September 26, 1997), ISBN-13: 978-0132610667
  • Kumar, K., Abhishek, B. (2012). Artificial Neural Networks for Diagnosis of Kidney Stones Disease. I.J. Information Technology and Computer Science, 2012, 7, 20-25 Published Online July 2012 in MECS (http://www.mecspress.org/) DOI: 10.5815/ijitcs.2012.07.03
  • Levey, A.S., Eckardt, K.U., Tsukamoto, Y, Levin, A., Coresh, J., Rossert, J., de Zeeuw, D., Hostetter, T.H. Lameire, N., Eknoyan, G. (2005). Definition and classification of chronic kidney disease: A position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney International, 67: 2089–2100.
  • Moradkhani, H., Hsu, K., Gupta, H.V., Sorooshian, S. (2004). Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. Journal of Hydrology,295(1–4):246–262.
  • National Kidney Foundation (2002). KJDOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis, volume 39, SI- 266, doi: 10.1053/ajkd.2002.30943
  • Öztemel, E. (2012). Yapay Sinir Ağları. Papatya Yayıncılık, 3. Basım Nisan 2012.
  • Soundarapandian, P. (2015). Senior Consultant Nephrologist, Apollo Hospitals, Karaikudi, Tamilnadu, India.
  • Suzuki, K. (2011). Artificial Neural Networks - Methodological Advances and Biomedical Applications. Published by In-Tech Janeza Trdine 9, 51000 Rijeka, Croatia. Copyright 2011 InTech.
  • Tanqi L., Liu Xun L., Ningshan L. , Xiaoming ,W. (2010). Application of radial basis function neural network to estimate glomerular filtration rate in Chinese patients with chronic kidney disease, 201O International Conference on Computer Application and System Modeling (ICCASM 2010)
  • Rouhani, M., Haghighi, M.M. (2009). The Diagnosis of Hepatitis diseases by Support Vector Machines and Artificial Neural Networks. International Association of Computer Science and Information Technology - Spring Conference 2009.
  • Tomar, D., Agarwal, S. (2015). Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes. Advances in Artificial Neural Systems Volume 2015, Article ID 265637, 10 pages http://dx.doi.org/10.1155/2015/265637
  • UCI (2015). https://archive.ics.uci.edu/ ml/datasets/ Chronic_Kidney_Disease. (Erişim tarihi: 17/09/2015).
Year 2015, Volume: 6 Issue: 2, 82 - 88, 31.12.2015

Abstract

References

  • Ababaei, B., Sohrabi, T., Mirzaei, F. (2012). Assessment of radial basis and generalized regression neural networks in Daily reservoir inflow simulation. Elixir Comp. Sci. & Engg. 42: 6074-6077.
  • Abhishek, B., Thakur, G.S.M., Gupta, D. (2012). Proposing Efficient Neural Network Training Model for Kidney Stone Diagnosis. (IJCSIT) International Journal of Computer Science and Information Technologies, 3 (3):3900-3904.
  • Afif, M.H., Hedar, A-R., Hamid, T.H.A., Mahdy, Y.B. (2013). SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis. (IJACSA) International Journal of Advanced Computer Science and Applications, 4(2):53-58.
  • Anthony, M., Bartlett, P.L. (2009). Neural Network Learning: Theoretical Foundation. Cambridge University Press.
  • Arisariyawong, S. A., Charoenseang, S. (2002). Dynamic self-organized learning for optimizing the complexity growth of radial basis function neural networks. Industrial Technology. IEEE ICIT'02. IEEE International Conference.
  • Delican, Y. , Özyilmaz, L., Yıldırım, T. (2011). Evolutionary Algorithms Based RBF Neural Networks For Parkinson s Disease Diagnosis. ELECO 2011 7th International Conference on Electrical and Electronics Engineering, 1-4 December, Bursa, TURKEY.
  • Graupe, D. (2013). Principles of Artificial Neural Networks: 3rd Edition (Advanced Series in Circuits & Systems) (Advanced Series in Circuits and Systems) . World Scientific Publishing Company; 3rd edition edition.
  • İlkuçar, M., Işık, A.H., Çifci, A. (2014). Classification of Breast Cancer Data with Harmony Search and Back Propagation Based Artificial Neural Network. IEEE, 2014 22 nd Signal Processing and Communications Applications Conference (SIU), 23-25 April 2014.
  • Jang, J-S.R., Sun, C.-T., Mizutani, E.,(1997), Neuro-Fuzzy and Soft Computing: A Computational Approach to Learningand Machine Intelligence. Prentice Hall; 1 edition (September 26, 1997), ISBN-13: 978-0132610667
  • Kumar, K., Abhishek, B. (2012). Artificial Neural Networks for Diagnosis of Kidney Stones Disease. I.J. Information Technology and Computer Science, 2012, 7, 20-25 Published Online July 2012 in MECS (http://www.mecspress.org/) DOI: 10.5815/ijitcs.2012.07.03
  • Levey, A.S., Eckardt, K.U., Tsukamoto, Y, Levin, A., Coresh, J., Rossert, J., de Zeeuw, D., Hostetter, T.H. Lameire, N., Eknoyan, G. (2005). Definition and classification of chronic kidney disease: A position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney International, 67: 2089–2100.
  • Moradkhani, H., Hsu, K., Gupta, H.V., Sorooshian, S. (2004). Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. Journal of Hydrology,295(1–4):246–262.
  • National Kidney Foundation (2002). KJDOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis, volume 39, SI- 266, doi: 10.1053/ajkd.2002.30943
  • Öztemel, E. (2012). Yapay Sinir Ağları. Papatya Yayıncılık, 3. Basım Nisan 2012.
  • Soundarapandian, P. (2015). Senior Consultant Nephrologist, Apollo Hospitals, Karaikudi, Tamilnadu, India.
  • Suzuki, K. (2011). Artificial Neural Networks - Methodological Advances and Biomedical Applications. Published by In-Tech Janeza Trdine 9, 51000 Rijeka, Croatia. Copyright 2011 InTech.
  • Tanqi L., Liu Xun L., Ningshan L. , Xiaoming ,W. (2010). Application of radial basis function neural network to estimate glomerular filtration rate in Chinese patients with chronic kidney disease, 201O International Conference on Computer Application and System Modeling (ICCASM 2010)
  • Rouhani, M., Haghighi, M.M. (2009). The Diagnosis of Hepatitis diseases by Support Vector Machines and Artificial Neural Networks. International Association of Computer Science and Information Technology - Spring Conference 2009.
  • Tomar, D., Agarwal, S. (2015). Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes. Advances in Artificial Neural Systems Volume 2015, Article ID 265637, 10 pages http://dx.doi.org/10.1155/2015/265637
  • UCI (2015). https://archive.ics.uci.edu/ ml/datasets/ Chronic_Kidney_Disease. (Erişim tarihi: 17/09/2015).
There are 20 citations in total.

Details

Primary Language Turkish
Journal Section Research Paper
Authors

Muhammer İlkuçar This is me

Publication Date December 31, 2015
Published in Issue Year 2015 Volume: 6 Issue: 2

Cite

APA İlkuçar, M. (2015). Kronik Böbrek Hastalarının Yapay Sinir Ağı ve Radyal Temelli Fonksiyon Ağı ile Teşhisi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(2), 82-88.