Öz
Kaynakça
- R. Medzhitov, “Origin and physiological roles of inflammation,” Nature, vol. 454, no. 7203. Nature Publishing Group, pp. 428–435, Jul. 24, 2008, doi: 10.1038/nature07201.
- D. R. Germolec, K. A. Shipkowski, R. P. Frawley, and E. Evans, “Markers of inflammation,” in Methods in Molecular Biology, vol. 1803, Humana Press Inc., 2018, pp. 57–79.
- D. D. Chaplin, “Overview of the immune response,” J. Allergy Clin. Immunol., vol. 125, no. 2 SUPPL. 2, pp. S3–S23, Feb. 2010, doi: 10.1016/j.jaci.2009.12.980.
- C. Gabay and I. Kushner, “Acute-Phase Proteins and Other Systemic Responses to Inflammation,” N. Engl. J. Med., vol. 340, no. 6, pp. 448–454, Feb. 1999, doi: 10.1056/NEJM199902113400607.
- G. F. Sonnenberg and D. Artis, “Innate lymphoid cells in the initiation, regulation and resolution of inflammation,” Nature Medicine, vol. 21, no. 7. Nature Publishing Group, pp. 698–708, Jul. 09, 2015, doi: 10.1038/nm.3892.
- E. Öztemel, Yapay Sinir Ağları, 2nd ed. Papatya Yayıncılık, 2006.
- S. S. Haykin, Neural Networks: A comprehensive Foundation. New Jersey: Prentice Hall, 1999.
- E. Guldogan, Z. Tunc, A. Acet, and C. Colak, “PERFORMANCE EVALUATION OF DIFFERENT ARTIFICIAL NEURAL NETWORK MODELS IN THE CLASSIFICATION OF TYPE 2 DIABETES MELLITUS,” J. Cogn. Syst., vol. 5, no. 1, pp. 23–32, Jun. 2020, Accessed: Apr. 11, 2021. [Online]. Available: http://dergipark.gov.tr/jcs.
- S. Elanayar and Y. C. Shin, “Radial Basis Function Neural Network for Approximation and Estimation of Nonlinear Stochastic Dynamic Systems,” IEEE Trans. Neural Networks, vol. 5, no. 4, pp. 594–603, 1994, doi: 10.1109/72.298229.
- E. Kiliç et al., “Lineer Olmayan Dinamik Sistemlerin Yapay Sinir Ağları ile Modellenmesinde MLP ve RBF Yapılarının Karşılaştırılması Comparison of MLP and RBF Structures in Modeling of Nonlinear Dynamic Systems with Artificial Neural Networks.”
- S. S. Haykin, Neural Networks and Learning Machines, 3rd ed., no. 10. c. New York: Prentice Hall, 2009.
- Y. S. Hwang and S. Y. Bang, “An efficient method to construct a radial basis function neural network classifier,” Neural Networks, vol. 10, no. 8, pp. 1495–1503, Nov. 1997, doi: 10.1016/S0893-6080(97)00002-6.
- Y. Lu, N. Sundararajan, and P. Saratchandran, “Performance evaluation of a sequential minimal Radial Basis Function (RBF) neural network learning algorithm,” IEEE Transactions on Neural Networks, vol. 9, no. 2. pp. 308–318, 1998, doi: 10.1109/72.661125.
- O. Kaynar, Y. Görmez, and F. Demirkoparan, “Değişik Kümeleme Algoritmalarıyla Eğitilmiş Radyal Tabanlı Yapay Sinir Ağlarıyla Saldırı Tespiti-Intrusion Detection with Radial Basis Neural Network Trained with Different Clust,” in International Artificial Intelligence and Data Processing Symposium (IDAP’16), 2016, pp. 167–173.
- E. Guldogan, Z. Tunc, A. Acet, and C. Colak, “PERFORMANCE EVALUATION OF DIFFERENT ARTIFICIAL NEURAL NETWORK MODELS IN THE CLASSIFICATION OF TYPE 2 DIABETES MELLITUS,” J. Cogn. Syst., vol. 5, no. 1, pp. 23–32, Jun. 2020, Accessed: Apr. 10, 2021. [Online]. Available: http://dergipark.gov.tr/jcs.
COMPUTER-AIDED MODEL FOR THE CLASSIFICATION OF ACUTE INFLAMMATIONS VIA RADIAL-BASED FUNCTION ARTIFICIAL NEURAL NETWORK
Öz
Abstract
Objective: This study aimed to compare the classification performance of acute inflammation by applying the RBF ANN model on an open-access acute inflammation data set and determining the risk factors that may be associated with acute inflammation markers.
Material and Methods: In the study, Nephritis of renal pelvis origin was classified using the open access “Acute Inflammation” data set RBF ANN model, and risk factors that could be associated were revealed. The success of RBF ANN is presented by different performance metrics.
Results: The success of classifying Nephritis of renal pelvis origin with the RBF ANN model has been demonstrated to be excellent (AUC = 1, Accuracy = 100%). In addition, the RBF ANN model revealed that the most important variable among the risk factors that may be associated with Nephritis of renal pelvis origin is “temperature of patient”.
Conclusion: As a result, the obtained findings show that the RBF ANN model provides very successful predictions in the classification of Nephritis of renal pelvis origin. Also, it has been shown that the importance values of factors associated with Nephritis of renal pelvis origin are estimated with the RBF classification model and can be used safely in preventive medicine applications.
Anahtar Kelimeler
Classification Radial-based function Artificial neural network Acute Inflammation
Kaynakça
- R. Medzhitov, “Origin and physiological roles of inflammation,” Nature, vol. 454, no. 7203. Nature Publishing Group, pp. 428–435, Jul. 24, 2008, doi: 10.1038/nature07201.
- D. R. Germolec, K. A. Shipkowski, R. P. Frawley, and E. Evans, “Markers of inflammation,” in Methods in Molecular Biology, vol. 1803, Humana Press Inc., 2018, pp. 57–79.
- D. D. Chaplin, “Overview of the immune response,” J. Allergy Clin. Immunol., vol. 125, no. 2 SUPPL. 2, pp. S3–S23, Feb. 2010, doi: 10.1016/j.jaci.2009.12.980.
- C. Gabay and I. Kushner, “Acute-Phase Proteins and Other Systemic Responses to Inflammation,” N. Engl. J. Med., vol. 340, no. 6, pp. 448–454, Feb. 1999, doi: 10.1056/NEJM199902113400607.
- G. F. Sonnenberg and D. Artis, “Innate lymphoid cells in the initiation, regulation and resolution of inflammation,” Nature Medicine, vol. 21, no. 7. Nature Publishing Group, pp. 698–708, Jul. 09, 2015, doi: 10.1038/nm.3892.
- E. Öztemel, Yapay Sinir Ağları, 2nd ed. Papatya Yayıncılık, 2006.
- S. S. Haykin, Neural Networks: A comprehensive Foundation. New Jersey: Prentice Hall, 1999.
- E. Guldogan, Z. Tunc, A. Acet, and C. Colak, “PERFORMANCE EVALUATION OF DIFFERENT ARTIFICIAL NEURAL NETWORK MODELS IN THE CLASSIFICATION OF TYPE 2 DIABETES MELLITUS,” J. Cogn. Syst., vol. 5, no. 1, pp. 23–32, Jun. 2020, Accessed: Apr. 11, 2021. [Online]. Available: http://dergipark.gov.tr/jcs.
- S. Elanayar and Y. C. Shin, “Radial Basis Function Neural Network for Approximation and Estimation of Nonlinear Stochastic Dynamic Systems,” IEEE Trans. Neural Networks, vol. 5, no. 4, pp. 594–603, 1994, doi: 10.1109/72.298229.
- E. Kiliç et al., “Lineer Olmayan Dinamik Sistemlerin Yapay Sinir Ağları ile Modellenmesinde MLP ve RBF Yapılarının Karşılaştırılması Comparison of MLP and RBF Structures in Modeling of Nonlinear Dynamic Systems with Artificial Neural Networks.”
- S. S. Haykin, Neural Networks and Learning Machines, 3rd ed., no. 10. c. New York: Prentice Hall, 2009.
- Y. S. Hwang and S. Y. Bang, “An efficient method to construct a radial basis function neural network classifier,” Neural Networks, vol. 10, no. 8, pp. 1495–1503, Nov. 1997, doi: 10.1016/S0893-6080(97)00002-6.
- Y. Lu, N. Sundararajan, and P. Saratchandran, “Performance evaluation of a sequential minimal Radial Basis Function (RBF) neural network learning algorithm,” IEEE Transactions on Neural Networks, vol. 9, no. 2. pp. 308–318, 1998, doi: 10.1109/72.661125.
- O. Kaynar, Y. Görmez, and F. Demirkoparan, “Değişik Kümeleme Algoritmalarıyla Eğitilmiş Radyal Tabanlı Yapay Sinir Ağlarıyla Saldırı Tespiti-Intrusion Detection with Radial Basis Neural Network Trained with Different Clust,” in International Artificial Intelligence and Data Processing Symposium (IDAP’16), 2016, pp. 167–173.
- E. Guldogan, Z. Tunc, A. Acet, and C. Colak, “PERFORMANCE EVALUATION OF DIFFERENT ARTIFICIAL NEURAL NETWORK MODELS IN THE CLASSIFICATION OF TYPE 2 DIABETES MELLITUS,” J. Cogn. Syst., vol. 5, no. 1, pp. 23–32, Jun. 2020, Accessed: Apr. 10, 2021. [Online]. Available: http://dergipark.gov.tr/jcs.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 29 Haziran 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 6 Sayı: 1 |
