Araştırma Makalesi
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Breast Cancer Diagnosis with Weighted Vote Based Ensemble Classification Algorithm

Yıl 2022, Cilt: 4 Sayı: 2, 112 - 120, 26.10.2022
https://doi.org/10.46387/bjesr.1092607

Öz

Breast cancer is a disease that is among the second causes of death among women, but its fatal risk is reduced with early diagnosis and the right treatment method. Currently, a large number of classification algorithms in data mining fields are adapted to breast cancer diagnosis based on patients' past medical records. With the help of these algorithms, the accuracy of diagnosis in diseases is significantly increased. In this study, a weighted vote-based ensemble classification algorithm is proposed for the diagnosis of breast cancer. The proposed algorithm is based on the working principle of more than one classification algorithm. By combining the classification algorithms with the weighted voting method, the result obtained from each algorithm alone is improved. The proposed weighted vote-based community classification algorithm consists of four stages. The first stage is the data preprocessing stage, followed by the classification stage. In the third stage, the reclassification process is carried out by using the weighted vote-based community classification algorithm with the performance values obtained from the classification process. With the proposed algorithm, an accuracy value of %98.77 was obtained, and a better value was obtained than the individual performance of each classification algorithm used in the classification phase.

Kaynakça

  • [1] The American Cancer Society, “What is Breast Cancer”. url: https://www.cancer.org/cancer/breast-cancer/about/what-is-breast-cancer.html. Accessed on 22/03/2022.
  • [2] T. S. Subashini, V. Ramalingam, and S. Palanivel, “Breast mass classification based on cytological patterns using RBFNN and SVM,” Expert Syst. Appl., vol. 36, no. 3, pp. 5284–5290, 2009.
  • [3] R. M. Levenson, E. A. Krupinski, V. M. Navarro, and E. A. Wasserman, “Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images.” PLoS One, 10(11), e0141357, 2015.
  • [4] M. F. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis,” Expert Syst. Appl., vol. 36, no. 2, pp. 3240– 3247, 2009.
  • [5] D. West, P. Mangiameli, R. Rampal, and V. West, “Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application,” Eur. J. Oper. Res., vol. 162, no. 2, pp. 532–551, 2005.
  • [6] S. Aruna, S. Rajagopalan, Nandakishore, “L. Knowledge based analysis of various statistical tools in detecting breast cancer” Comput. Sci. Inf. Technol. 2, 37–45, 2011.
  • [7] V. Chaurasia, S. Pal, “Data mining techniques: To predict and resolve breast cancer survivability” Int. J. Comput. Sci. Mob. Comput., 3, 10–22, 2011.
  • [8] H. Asri, H. Mousannif, H. Al Moatassime, T. Noel, “Using machine learning algorithms for breast cancer risk prediction and diagnosis.” Procedia Comput. Sci. 83, 1064–1069, 2016.
  • [9] D. Wang, D. Zhang and Y. H. Huang “Breast Cancer Prediction Using Machine Learning”, Vol. 66, no.7, 2018
  • [10] M.K Keles, M. Kaya, "Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study." Tehnicki Vjesnik - Technical Gazette, vol. 26, no. 1, p. 149, 2019.
  • [11] R. K. Kavitha1, D. D. Rangasamy, “Breast Cancer Survivability Using Adaptive Voting Ensemble Machine Learning Algorithm Adaboost and CART Algorithm” Volume 3, Special Issue 1, 2014.
  • [12] L.G. Ahmad, A. Eshlaghy, A. Poorebrahimi, M. Ebrahimi, A. Razavi, and others. “Using three machine learning techniques for predicting breast cancer recurrence.” J Health Med Inform, 4, 3, 2013.
  • [13] A. Alharbi, F. Tchier, “Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on Saudi Arabian breast cancer database”, Mathematical Biosciences, 286, 39-48, 2017.
  • [14] M.M Ghiasi, S. Zendehboudi, “Application of decision tree-based ensemble learning in the classification of breast cancer”, Computers in Biology and Medicine, Vol. 128, 104089, 2021.
  • [15] P. Ghosh, A. Karim, S. T. Atik, S. Afrin, M. Saifuzzaman, “Expert cancer model using supervised algorithms with a LASSO selection approach”, International Journal of Electrical and Computer Engineering, Vol. 11, no. 3, pp. 2631-2639, 2021.
  • [16] J. Han and M. Kamber, “Data Mining Concepts and Techniques”, Morgan Kauffman Publishers, (2000).
  • [17] P. Cabena, P. Hadjinian, R. Stadler, J. Verhees, and A. Zanasi, “Discovering Data Mining: From Concept to Implementation”, Upper Saddle River, N.J., Prentice Hall, 1998.
  • [18] A. S. Assiri, S. Nazir, and S. A. Velastin, “Breast Tumor Classification Using an Ensemble Machine Learning Method.” Journal of Imaging, 6(6), 39, 2020.
  • [19] T. Mitchell, “Machine Learning”. New York, USA, McGraw Hill, 1997.
  • [20] P. Harrington, “Machine Learning in Action.” New York, USA, Manning Publications, 2012.
  • [21] S. Aruna, S. Rajagopalan, L. Nandakishore, “Knowledge based analysis of various statistical tools in detecting breast cancer.” Comput. Sci. Inf. Technol., 2, 37–45, 2011.
  • [22] H. Asri, H. Mousannif, H. Al Moatassime, T. Noel, “Using machine learning algorithms for breast cancer risk prediction and diagnosis.” Procedia Comput. Sci. 83, 1064–1069, 2016.
  • [23] D. Delen, G. Walker, A. Kadam, “Predicting breast cancer survivability: A comparison of three data mining methods.” Artif. Intell. Med., 34, 113–127, 2005.
  • [24] T. Al-Quraishi, J.H. Abawajy, M.U. Chowdhury, S. Rajasegarar, A.S. Abdalrada, “Breast cancer recurrence prediction using random forest model”, in: Adv. In- tell. Syst. Comput., pp. 318–329, 2018.
  • [25] N. Shukla, M. Hagenbuchner, K.T. Win, J. Yang, “Breast cancer data analysis for survivability studies and prediction. Comput. Methods Programs Biomed.”, 155, 199–208, 2018.
  • [26] K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, D.I. Fotiadis, “Machine learning applications in cancer prognosis and prediction.” Comput. Struct. Biotechnol. J., 13, 8–17, 2015.

Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması ile Göğüs Kanseri Teşhisi

Yıl 2022, Cilt: 4 Sayı: 2, 112 - 120, 26.10.2022
https://doi.org/10.46387/bjesr.1092607

Öz

Meme kanseri, kadınlar arasında ikinci ölüm nedenleri arasında gösterilen fakat erken teşhis ve ardından uygulanan doğru tedavi yöntemi ile ölümcül riski azaltılan bir hastalıktır. Günümüzde, veri madenciliği alanlarındaki çok sayıda sınıflandırma algoritması, hastaların geçmiş tıbbi kayıtlarına dayalı olarak meme kanseri teşhisine uyarlanmaktadır. Bu algoritmaların yardımı ile hastalıklardaki teşhis doğruluğu önemli ölçüde artırılmaktadır. Bu çalışmada, meme kanseri tanısı için ağırlıklı oy tabanlı topluluk sınıflandırma algoritması önerilmektedir. Önerilen algoritma, birden fazla sınıflandırma algoritmasının bir arada çalışma prensibine dayanmaktadır. Sınıflandırma algoritmaları ağırlıklı oylama yöntemi ile bir araya getirilerek her bir algoritmadan tek başına elde edilen sonucun iyileştirilmesi sağlanmaktadır. Önerilen ağırlıklı oy tabanlı topluluk sınıflandırma algoritması dört aşamadan oluşmaktadır. İlk aşama veri önişleme aşaması olup bu aşamayı sınıflandırma aşaması izlemektedir. Üçüncü aşamada, sınıflandırma işleminden elde edilen performans değerleri ile ağırlıklı oy tabanlı topluluk sınıflandırma algoritması kullanılarak yeniden sınıflandırma işlemi gerçekleştirilmektir. Önerilen algoritma ile %98.77 doğruluk değeri elde edilerek sınıflandırma aşamasında kullanılan her bir sınıflandırma algoritmasının bireysel performansından daha iyi bir değer elde edilmiştir.

Kaynakça

  • [1] The American Cancer Society, “What is Breast Cancer”. url: https://www.cancer.org/cancer/breast-cancer/about/what-is-breast-cancer.html. Accessed on 22/03/2022.
  • [2] T. S. Subashini, V. Ramalingam, and S. Palanivel, “Breast mass classification based on cytological patterns using RBFNN and SVM,” Expert Syst. Appl., vol. 36, no. 3, pp. 5284–5290, 2009.
  • [3] R. M. Levenson, E. A. Krupinski, V. M. Navarro, and E. A. Wasserman, “Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images.” PLoS One, 10(11), e0141357, 2015.
  • [4] M. F. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis,” Expert Syst. Appl., vol. 36, no. 2, pp. 3240– 3247, 2009.
  • [5] D. West, P. Mangiameli, R. Rampal, and V. West, “Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application,” Eur. J. Oper. Res., vol. 162, no. 2, pp. 532–551, 2005.
  • [6] S. Aruna, S. Rajagopalan, Nandakishore, “L. Knowledge based analysis of various statistical tools in detecting breast cancer” Comput. Sci. Inf. Technol. 2, 37–45, 2011.
  • [7] V. Chaurasia, S. Pal, “Data mining techniques: To predict and resolve breast cancer survivability” Int. J. Comput. Sci. Mob. Comput., 3, 10–22, 2011.
  • [8] H. Asri, H. Mousannif, H. Al Moatassime, T. Noel, “Using machine learning algorithms for breast cancer risk prediction and diagnosis.” Procedia Comput. Sci. 83, 1064–1069, 2016.
  • [9] D. Wang, D. Zhang and Y. H. Huang “Breast Cancer Prediction Using Machine Learning”, Vol. 66, no.7, 2018
  • [10] M.K Keles, M. Kaya, "Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study." Tehnicki Vjesnik - Technical Gazette, vol. 26, no. 1, p. 149, 2019.
  • [11] R. K. Kavitha1, D. D. Rangasamy, “Breast Cancer Survivability Using Adaptive Voting Ensemble Machine Learning Algorithm Adaboost and CART Algorithm” Volume 3, Special Issue 1, 2014.
  • [12] L.G. Ahmad, A. Eshlaghy, A. Poorebrahimi, M. Ebrahimi, A. Razavi, and others. “Using three machine learning techniques for predicting breast cancer recurrence.” J Health Med Inform, 4, 3, 2013.
  • [13] A. Alharbi, F. Tchier, “Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on Saudi Arabian breast cancer database”, Mathematical Biosciences, 286, 39-48, 2017.
  • [14] M.M Ghiasi, S. Zendehboudi, “Application of decision tree-based ensemble learning in the classification of breast cancer”, Computers in Biology and Medicine, Vol. 128, 104089, 2021.
  • [15] P. Ghosh, A. Karim, S. T. Atik, S. Afrin, M. Saifuzzaman, “Expert cancer model using supervised algorithms with a LASSO selection approach”, International Journal of Electrical and Computer Engineering, Vol. 11, no. 3, pp. 2631-2639, 2021.
  • [16] J. Han and M. Kamber, “Data Mining Concepts and Techniques”, Morgan Kauffman Publishers, (2000).
  • [17] P. Cabena, P. Hadjinian, R. Stadler, J. Verhees, and A. Zanasi, “Discovering Data Mining: From Concept to Implementation”, Upper Saddle River, N.J., Prentice Hall, 1998.
  • [18] A. S. Assiri, S. Nazir, and S. A. Velastin, “Breast Tumor Classification Using an Ensemble Machine Learning Method.” Journal of Imaging, 6(6), 39, 2020.
  • [19] T. Mitchell, “Machine Learning”. New York, USA, McGraw Hill, 1997.
  • [20] P. Harrington, “Machine Learning in Action.” New York, USA, Manning Publications, 2012.
  • [21] S. Aruna, S. Rajagopalan, L. Nandakishore, “Knowledge based analysis of various statistical tools in detecting breast cancer.” Comput. Sci. Inf. Technol., 2, 37–45, 2011.
  • [22] H. Asri, H. Mousannif, H. Al Moatassime, T. Noel, “Using machine learning algorithms for breast cancer risk prediction and diagnosis.” Procedia Comput. Sci. 83, 1064–1069, 2016.
  • [23] D. Delen, G. Walker, A. Kadam, “Predicting breast cancer survivability: A comparison of three data mining methods.” Artif. Intell. Med., 34, 113–127, 2005.
  • [24] T. Al-Quraishi, J.H. Abawajy, M.U. Chowdhury, S. Rajasegarar, A.S. Abdalrada, “Breast cancer recurrence prediction using random forest model”, in: Adv. In- tell. Syst. Comput., pp. 318–329, 2018.
  • [25] N. Shukla, M. Hagenbuchner, K.T. Win, J. Yang, “Breast cancer data analysis for survivability studies and prediction. Comput. Methods Programs Biomed.”, 155, 199–208, 2018.
  • [26] K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, D.I. Fotiadis, “Machine learning applications in cancer prognosis and prediction.” Comput. Struct. Biotechnol. J., 13, 8–17, 2015.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yapay Zeka, Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Sinem Bozkurt Keser 0000-0002-8013-6922

Kemal Keskin 0000-0002-3969-2396

Yayımlanma Tarihi 26 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 4 Sayı: 2

Kaynak Göster

APA Bozkurt Keser, S., & Keskin, K. (2022). Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması ile Göğüs Kanseri Teşhisi. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 4(2), 112-120. https://doi.org/10.46387/bjesr.1092607
AMA Bozkurt Keser S, Keskin K. Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması ile Göğüs Kanseri Teşhisi. Müh.Bil.ve Araş.Dergisi. Ekim 2022;4(2):112-120. doi:10.46387/bjesr.1092607
Chicago Bozkurt Keser, Sinem, ve Kemal Keskin. “Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması Ile Göğüs Kanseri Teşhisi”. Mühendislik Bilimleri Ve Araştırmaları Dergisi 4, sy. 2 (Ekim 2022): 112-20. https://doi.org/10.46387/bjesr.1092607.
EndNote Bozkurt Keser S, Keskin K (01 Ekim 2022) Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması ile Göğüs Kanseri Teşhisi. Mühendislik Bilimleri ve Araştırmaları Dergisi 4 2 112–120.
IEEE S. Bozkurt Keser ve K. Keskin, “Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması ile Göğüs Kanseri Teşhisi”, Müh.Bil.ve Araş.Dergisi, c. 4, sy. 2, ss. 112–120, 2022, doi: 10.46387/bjesr.1092607.
ISNAD Bozkurt Keser, Sinem - Keskin, Kemal. “Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması Ile Göğüs Kanseri Teşhisi”. Mühendislik Bilimleri ve Araştırmaları Dergisi 4/2 (Ekim 2022), 112-120. https://doi.org/10.46387/bjesr.1092607.
JAMA Bozkurt Keser S, Keskin K. Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması ile Göğüs Kanseri Teşhisi. Müh.Bil.ve Araş.Dergisi. 2022;4:112–120.
MLA Bozkurt Keser, Sinem ve Kemal Keskin. “Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması Ile Göğüs Kanseri Teşhisi”. Mühendislik Bilimleri Ve Araştırmaları Dergisi, c. 4, sy. 2, 2022, ss. 112-20, doi:10.46387/bjesr.1092607.
Vancouver Bozkurt Keser S, Keskin K. Ağırlıklı Oy Tabanlı Topluluk Sınıflandırma Algoritması ile Göğüs Kanseri Teşhisi. Müh.Bil.ve Araş.Dergisi. 2022;4(2):112-20.