___

• 1. Breast Cancer Awareness Month 2021 – IARC [Internet]. [cited 2022 Feb 14]. Available from: https://www.iarc.who.int/featured-news/breast-cancer-awareness-month-2021/
• 2. Palhazi P. Gross Anatomy of the Breast and Axilla. Breast Cancer Manag Surg [Internet]. 2018 [cited 2022 Sep 19];3–10. Available from: https://link.springer.com/chapter/10.1007/978-3-319-56673-3_1
• 3. Açikgöz A, AKAL yILDIZ E, Üniversitesi H, Bilimleri Fakültesi S, ve Diyetetik Bölümü B, Sorumlu yazar A, et al. Meme Kanseri Etiyolojisi ve Risk Faktörleri Etiology and risk Factors of Breast Cancer. [cited 2022 Sep 19]; Available from: http://www.ncbi.nlm.nih.gov/
• 4. Neriman Bayraktar NG. Kadınların Meme Kanserinin Erken Tanısına Yönelik Farkındalıklarının ve Uygulamalarının Belirlenmesi Determination of Women’s Awareness and Practices on Early Diagnosis of Breast Cancer (Araştırma). Üniversitesi, Hacettepe Dergisi, Hemşirelik Fakültesi Güzel. 6(2):101–10.
• 5. Kumari M, Singh V. Breast Cancer Prediction system. Procedia Comput Sci. 2018 Jan 1;132:371–6.
• 6. Levenson RM, Krupinski EA, Navarro VM, Wasserman EA. Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images. PLoS One. 2015 Nov 1;10(11).
• 7. Rani KU. Parallel Approach for Diagnosis of Breast Cancer using Neural Network Technique. Int J Comput Appl. 2010 Sep 10;10(3):1–5
• 8. Yavuz E, Eyupoglu C, Sanver U, Yazici R. An ensemble of neural networks for breast cancer diagnosis. 2nd Int Conf Comput Sci Eng UBMK 2017. 2017 Oct 31;538–43.
• 9. Mert A, Kiliç N, Bilgili E, Akan A. Breast Cancer Detection with Reduced Feature Set. Comput Math Methods Med [Internet]. 2015 [cited 2022 Mar 25];2015. Available from: /pmc/articles/PMC4452509/
• 10. Wang S, Wang Y, Wang D, Yin Y, Wang Y, Jin Y. An improved random forest-based rule extraction method for breast cancer diagnosis. Appl Soft Comput. 2020 Jan 1;86:105941.
• 11. Yue W, Wang Z, Chen H, Payne A, Liu X. Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis. Des 2018, Vol 2, Page 13 [Internet]. 2018 May 9 [cited 2022 Apr 28];2(2):13. Available from: https://www.mdpi.com/2411-9660/2/2/13/htm
• 12. Zor K, Çelik Ö, Timur O, Teke A. Short-term building electrical energy consumption forecasting by employing gene expression programming and GMDH networks. Energies. 2020 Mar 1;13(5).
• 13. Ferreira C, Leandro I, De Castro N, Zuben FJ Von. GEP AND THE EVOLUTION OF COMPUTER PROGRAMS GENE EXPRESSION PROGRAMMING AND THE EVOLUTION OF COMPUTER PROGRAMS. 2004;82–103.
• 14. Sumbaly R, Vishnusri N, Jeyalatha S. Diagnosis of Breast Cancer using Decision Tree Data Mining Technique. Int J Comput Appl. 2014 Jul 18;98(10):16–24.
• 15. Akgündogdu A. BREAST CANCER CLASSIFICATION WITH GENETIC PROGRAMMING. Int J Electron Mech MECHATRONICS Eng. 2:72–8.
• 16. Fatih Akay M. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl [Internet]. [cited 2022 Mar 25];36:3240–7. Available from: www.imaginis.com/breasthealth/breast_cancer.asp,
• 17. Makalesi Meme Kanseri Teşhisi İçin Yeni Bir Skor Füzyon Yaklaşımı Erdem YAVUZ A, EYÜPOĞLU Bilgisayar Mühendisliği Bölümü C, ve Doğa Bilimleri Fakültesi M, Teknik Üniversitesi B, Bilgisayar Mühendisliği Bölümü T, Harp Okulu H, et al. Meme Kanseri Teşhisi İçin Yeni Bir Skor Füzyon Yaklaşımı. Düzce Üniversitesi Bilim ve Teknol Derg [Internet]. 2019 Jul 31 [cited 2022 Mar 7];7(3):1045–60. Available from: https://dergipark.org.tr/tr/pub/dubited/issue/46290/488460
• 18. Fred Agarap AM. On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset. 2018 [cited 2022 Feb 8]; Available from: https://doi.org/10.1145/3184066.3184080
• 19. Hamsagayathri P, Sampath P. PERFORMANCE ANALYSIS OF BREAST CANCER CLASSIFICATION USING DECISION TREE CLASSIFIERS. Int J Curr Pharm Res. 2017 Mar 1;9(2):19.
• 20. Kiyan T, Yildirim T. BREAST CANCER DIAGNOSIS USING STATISTICAL NEURAL NETWORKS.
• 21. UCI Machine Learning Repository: Breast Cancer Wisconsin (Diagnostic) Data Set [Internet]. [cited 2022 Feb 14]. Available from: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
• 22. Salama G, Abdelhalim MB, Zeid MA. Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers. 2012;
• 23. Breast Cancer Wisconsin (Diagnostic) Data Set | Kaggle [Internet]. [cited 2022 Feb 17]. Available from: https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
• 24. Specht DF. A General Regression Neural Network. IEEE Trans Neural Networks. 1991;2(6):568–76.
• 25. Hong H, Zhang Z, Guo A, Shen L, Sun H, Liang Y, et al. Radial basis function artificial neural network (RBF ANN) as well as the hybrid method of RBF ANN and grey relational analysis able to well predict trihalomethanes levels in tap water. J Hydrol. 2020 Dec 1;591:125574.
• 26. Osuna E, Freund R, Girosi F. Training support vector machines: An application to face detection. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 1997;130–6.
• 27. Pontil M, Verri A. Support vector machines for 3D object recognition. IEEE Trans Pattern Anal Mach Intell. 1998;20(6):637–46.
• 28. Schölkopf B, Sung KK, Burges CJC, Girosi F, Niyogi P, Poggio T, et al. Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process. 1997;45(11):2758–65.
• 29. Wan V, Campbell WM. Support vector machines for speaker verification and identification. Neural Networks Signal Process - Proc IEEE Work. 2000;2:775–84.
• 30. Korkut O, Fen AÜ, Enstitüsü B, Kuşcu AC, Erol H, Tarihçesi M. Diagnosis of Breast Cancer by K-Mean Clustering and Otsu Thresholding Segmentation Methods. Osmaniye Korkut Ata Üniversitesi Fen Bilim Enstitüsü Derg [Internet]. 2022 Mar 8 [cited 2022 May 13];5(1):258–81. Available from: https://dergipark.org.tr/tr/pub/okufbed/issue/68798/994481