Research Article
BibTex RIS Cite

Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem

Year 2022, Volume: 34 Issue: 1, 75 - 84, 20.03.2022
https://doi.org/10.35234/fumbd.962824

Abstract

Böbrekler, sağlıklı bir yaşam sürdürebilmemiz için gerekli olan bir takım işlevleri yerine getirmektedir. Kronik Böbrek Hastalığı (KBH), böbreklerin görevini yerine getiremediği durumlarda ortaya çıkmaktadır. KBH verileri UCI makine öğrenimi veri tabanından alınmıştır. KBH veri seti 400 kişiye ait 24 farklı özellikten oluşmaktadır. Bu çalışmada, KBH tanısı için Genetik Algoritma - Dalgacık Çekirdeği – Uç Öğrenme Makinesi (GA-DÇ-UÖM) yöntemi uygulanmıştır. Geliştirilen DÇ-UÖM sınıflandırıcısı, KBH verilerini sınıflandırmak için kullanılmıştır. GA, UÖM 'nin gizli katman nöron sayısını ve Dalgacık Çekirdeğinin k, l, m parametre değerlerini optimize etmek için kullanılmıştır. Ayrıca, DÇ-UÖM 'nin sınıflandırma performansını artırmak için GA uygulanmaktadır. 400*24 öznitelik vektörü, DÇ-UÖM sınıflandırıcısına girdi olarak verilmiştir. GA-DÇ-UÖM 'nin başarısı, sınıflandırma doğruluğu, duyarlılık ve özgüllük analizi kullanılarak hesaplanmıştır. Önerilen GA-DÇ-UÖM yönteminin sınıflandırma başarısı %98,42 olarak hesaplanmıştır. Dalgacık çekirdek fonksiyonunun k, l, m parametrelerinin optimal değerleri sırasıyla 5, 3 ve 12 olarak bulunmuştur. Gizli katman nöron sayısının optimum değeri 220 olarak bulunmuştur.

References

  • [1] Levey, A. S., Eckardt, K. U., Tsukamoto, Y., Levin, A., Coresh, J., Rossert, J., ... & Eknoyan, G. (2005). Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney international, 67(6), 2089-2100.
  • [2] Türkiye’de, Nefroloji-Diyaliz ve Transplantasyon. Registry 2009 İstanbul: Türk Nefroloji Derneği 2010.
  • [3] Süleymanlar, G., Utaş, C., Arinsoy, T., Ateş, K., Altun, B., Altiparmak, M. R., ... & Serdengeçti, K. (2010). A population-based survey of Chronic REnal Disease In Turkey—the CREDIT study. Nephrology Dialysis Transplantation, 26(6), 1862-1871.
  • [4] Tanrıverdi, M. H. (2010). Kronik böbrek yetmezliği. Konuralp tıp dergisi, 2010(2), 27-32.
  • [5] Gunarathne, W. H. S. D., Perera, K. D. M., & Kahandawaarachchi, K. A. D. C. P. (2017, October). Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD). In Bioinformatics and Bioengineering (BIBE), 2017 IEEE 17th International Conference on (pp. 291-296). IEEE.
  • [6] Charleonnan, A., Fufaung, T., Niyomwong, T., Chokchueypattanakit, W., Suwannawach, S., & Ninchawee, N. (2016, October). Predictive analytics for chronic kidney disease using machine learning techniques. In Management and Innovation Technology International Conference (MITicon), 2016(pp. MIT-80). IEEE. [7] Sharma, S., Sharma, V., & Sharma, A. (2016). Performance Based Evaluation of VariousMachine Learning Classification Techniques for Chronic Kidney Disease Diagnosis. arXiv preprint arXiv:1606.09581.
  • [8] Yildirim, P. (2017, July). Chronic Kidney Disease Prediction on Imbalanced Data by Multilayer Perceptron: Chronic Kidney Disease Prediction. In Computer Software and Applications Conference (COMPSAC), 2017 IEEE 41st Annual (Vol. 2, pp. 193-198). IEEE.
  • [9] Ahmad, M., Tundjungsari, V., Widianti, D., Amalia, P., & Rachmawati, U. A. (2017, November). Diagnostic decision support system of chronic kidney disease using support vector machine. In Informatics and Computing (ICIC), 2017 Second International Conference on (pp. 1-4) IEEE.
  • [10] Sinha, P., & Sinha, P. (2015). Comparative study of chronic kidney disease prediction using KNN and SVM. International Journal of Engineering Research and Technology, 4(12), 608-12. [11] Huang, G. B., Ding, X., & Zhou, H. (2010). Optimization method based extreme learning machine for classification. Neurocomputing, 74(1-3), 155-163.
  • [12] Zhu, Q. Y., Qin, A. K., Suganthan, P. N., & Huang, G. B. (2005). Evolutionary extreme learning machine. Pattern recognition, 38(10), 1759-1763.
  • [13] Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
  • [14] Suresh, S., Saraswathi, S., & Sundararajan, N. (2010). Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Engineering Applications of Artificial Intelligence, 23(7), 1149-1157.
  • [15] Li, B., Rong, X., & Li, Y. (2014). An improved kernel based extreme learning machine for robot execution failures. The Scientific World Journal, 2014.
  • [16] Qi, L., & Jiang, H. (1997). Semismooth Karush-Kuhn-Tucker equations and convergence analysis of Newton and quasi- Newton methods for solving these equations. Mathematics of Operations Research, 22(2), 301-325.
  • [17] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • [18] Ertam, F., & Avcı, E. (2017). A new approach for internet traffic classification: GA-WK-ELM. Measurement, 95, 135-142.
  • [19] Ding, S., Zhang, J., Xu, X., & Zhang, Y. (2016). A wavelet extreme learning machine. Neural Computing and Applications, 27(4), 1033-1040.
  • [20] K.,Kayaer, & T. Yıldırım, (2003). Medical diagnosis on pima indian diabetes using general regression neural networks, artificial neural networks and neural information processing (ICANN/ICONIP) (pp. 181–184), Istanbul, Turkey, June 26–29.
  • [21] Bin Li, Xuewen Rong and Yibin Li, “An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures”, Hindawi Publishing Corporation The Scientific World Journal, Volume 2014, Article ID 906546, pp. 7, http://dx.doi.org/10.1155/2014/906546.
  • [22] Avci, D. (2016). An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine. Journal of Electrical Engineering & Technology, 11(4), 993-1002.
  • [23] Beasley, D., Bull, D. R., & Martin, R. R. (1993). An overview of genetic algorithms:Part1, fundamentals. University computing, 15(2),5669.
  • [24] Goldberg, D.E.(2006). Genetic algorithms. Pearson Education India.
  • [25] Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K., ... & Morris, Q. (2015). The human splicing code reveals new insights into the genetic determinants of disease. Science, 347(6218), 1254806.
  • [26] Edwards, A. L., Hammond, H. A., Jin, L., Caskey, C. T., & Chakraborty, R. (1992). Genetic variation at five trimeric and tetrameric tandem repeat loci in four human population groups. Genomics, 12(2), 241-253.
  • [27] Mirjalili, S. (2019). Genetic algorithm. In Evolutionary algorithms and neural networks (pp. 43-55). Springer, Cham.
  • [28] https://archive.ics.uci.edu/ml/datasets/chronickidn ey_disease last accessed: September 13, 2020.
  • [29] Matlab R2018b, MATLAB Company, 2018.
  • [30] Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., & Müller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC bioinformatics, 12(1), 77.
  • [31] Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localization in cluster inference. Neuroimage, 44(1), 83-98.
Year 2022, Volume: 34 Issue: 1, 75 - 84, 20.03.2022
https://doi.org/10.35234/fumbd.962824

Abstract

References

  • [1] Levey, A. S., Eckardt, K. U., Tsukamoto, Y., Levin, A., Coresh, J., Rossert, J., ... & Eknoyan, G. (2005). Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney international, 67(6), 2089-2100.
  • [2] Türkiye’de, Nefroloji-Diyaliz ve Transplantasyon. Registry 2009 İstanbul: Türk Nefroloji Derneği 2010.
  • [3] Süleymanlar, G., Utaş, C., Arinsoy, T., Ateş, K., Altun, B., Altiparmak, M. R., ... & Serdengeçti, K. (2010). A population-based survey of Chronic REnal Disease In Turkey—the CREDIT study. Nephrology Dialysis Transplantation, 26(6), 1862-1871.
  • [4] Tanrıverdi, M. H. (2010). Kronik böbrek yetmezliği. Konuralp tıp dergisi, 2010(2), 27-32.
  • [5] Gunarathne, W. H. S. D., Perera, K. D. M., & Kahandawaarachchi, K. A. D. C. P. (2017, October). Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD). In Bioinformatics and Bioengineering (BIBE), 2017 IEEE 17th International Conference on (pp. 291-296). IEEE.
  • [6] Charleonnan, A., Fufaung, T., Niyomwong, T., Chokchueypattanakit, W., Suwannawach, S., & Ninchawee, N. (2016, October). Predictive analytics for chronic kidney disease using machine learning techniques. In Management and Innovation Technology International Conference (MITicon), 2016(pp. MIT-80). IEEE. [7] Sharma, S., Sharma, V., & Sharma, A. (2016). Performance Based Evaluation of VariousMachine Learning Classification Techniques for Chronic Kidney Disease Diagnosis. arXiv preprint arXiv:1606.09581.
  • [8] Yildirim, P. (2017, July). Chronic Kidney Disease Prediction on Imbalanced Data by Multilayer Perceptron: Chronic Kidney Disease Prediction. In Computer Software and Applications Conference (COMPSAC), 2017 IEEE 41st Annual (Vol. 2, pp. 193-198). IEEE.
  • [9] Ahmad, M., Tundjungsari, V., Widianti, D., Amalia, P., & Rachmawati, U. A. (2017, November). Diagnostic decision support system of chronic kidney disease using support vector machine. In Informatics and Computing (ICIC), 2017 Second International Conference on (pp. 1-4) IEEE.
  • [10] Sinha, P., & Sinha, P. (2015). Comparative study of chronic kidney disease prediction using KNN and SVM. International Journal of Engineering Research and Technology, 4(12), 608-12. [11] Huang, G. B., Ding, X., & Zhou, H. (2010). Optimization method based extreme learning machine for classification. Neurocomputing, 74(1-3), 155-163.
  • [12] Zhu, Q. Y., Qin, A. K., Suganthan, P. N., & Huang, G. B. (2005). Evolutionary extreme learning machine. Pattern recognition, 38(10), 1759-1763.
  • [13] Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501.
  • [14] Suresh, S., Saraswathi, S., & Sundararajan, N. (2010). Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Engineering Applications of Artificial Intelligence, 23(7), 1149-1157.
  • [15] Li, B., Rong, X., & Li, Y. (2014). An improved kernel based extreme learning machine for robot execution failures. The Scientific World Journal, 2014.
  • [16] Qi, L., & Jiang, H. (1997). Semismooth Karush-Kuhn-Tucker equations and convergence analysis of Newton and quasi- Newton methods for solving these equations. Mathematics of Operations Research, 22(2), 301-325.
  • [17] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • [18] Ertam, F., & Avcı, E. (2017). A new approach for internet traffic classification: GA-WK-ELM. Measurement, 95, 135-142.
  • [19] Ding, S., Zhang, J., Xu, X., & Zhang, Y. (2016). A wavelet extreme learning machine. Neural Computing and Applications, 27(4), 1033-1040.
  • [20] K.,Kayaer, & T. Yıldırım, (2003). Medical diagnosis on pima indian diabetes using general regression neural networks, artificial neural networks and neural information processing (ICANN/ICONIP) (pp. 181–184), Istanbul, Turkey, June 26–29.
  • [21] Bin Li, Xuewen Rong and Yibin Li, “An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures”, Hindawi Publishing Corporation The Scientific World Journal, Volume 2014, Article ID 906546, pp. 7, http://dx.doi.org/10.1155/2014/906546.
  • [22] Avci, D. (2016). An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine. Journal of Electrical Engineering & Technology, 11(4), 993-1002.
  • [23] Beasley, D., Bull, D. R., & Martin, R. R. (1993). An overview of genetic algorithms:Part1, fundamentals. University computing, 15(2),5669.
  • [24] Goldberg, D.E.(2006). Genetic algorithms. Pearson Education India.
  • [25] Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K., ... & Morris, Q. (2015). The human splicing code reveals new insights into the genetic determinants of disease. Science, 347(6218), 1254806.
  • [26] Edwards, A. L., Hammond, H. A., Jin, L., Caskey, C. T., & Chakraborty, R. (1992). Genetic variation at five trimeric and tetrameric tandem repeat loci in four human population groups. Genomics, 12(2), 241-253.
  • [27] Mirjalili, S. (2019). Genetic algorithm. In Evolutionary algorithms and neural networks (pp. 43-55). Springer, Cham.
  • [28] https://archive.ics.uci.edu/ml/datasets/chronickidn ey_disease last accessed: September 13, 2020.
  • [29] Matlab R2018b, MATLAB Company, 2018.
  • [30] Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., & Müller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC bioinformatics, 12(1), 77.
  • [31] Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localization in cluster inference. Neuroimage, 44(1), 83-98.
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section MBD
Authors

Derya Avcı 0000-0002-5204-0501

Akif Doğantekin This is me

Publication Date March 20, 2022
Submission Date July 5, 2021
Published in Issue Year 2022 Volume: 34 Issue: 1

Cite

APA Avcı, D., & Doğantekin, A. (2022). Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(1), 75-84. https://doi.org/10.35234/fumbd.962824
AMA Avcı D, Doğantekin A. Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. March 2022;34(1):75-84. doi:10.35234/fumbd.962824
Chicago Avcı, Derya, and Akif Doğantekin. “Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34, no. 1 (March 2022): 75-84. https://doi.org/10.35234/fumbd.962824.
EndNote Avcı D, Doğantekin A (March 1, 2022) Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34 1 75–84.
IEEE D. Avcı and A. Doğantekin, “Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 34, no. 1, pp. 75–84, 2022, doi: 10.35234/fumbd.962824.
ISNAD Avcı, Derya - Doğantekin, Akif. “Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34/1 (March 2022), 75-84. https://doi.org/10.35234/fumbd.962824.
JAMA Avcı D, Doğantekin A. Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34:75–84.
MLA Avcı, Derya and Akif Doğantekin. “Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 34, no. 1, 2022, pp. 75-84, doi:10.35234/fumbd.962824.
Vancouver Avcı D, Doğantekin A. Kronik Böbrek Hastalığının Teşhisi İçin Genetik Algoritma Dalgacık Çekirdeği Uç Öğrenme Makinesine Dayalı Uzman Bir Sistem. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;34(1):75-84.