Makine Öğrenimi Teknikleri ile Göğüs Kanserinin Teşhisi

Year 2022, Volume: 11 Issue: 2, 594 - 603, 30.06.2022

Halime Doğan , Ahmet Tatar , Alper Kadir Tanyıldızı , Beyda Taşar

https://doi.org/10.17798/bitlisfen.1065685

Cited By: 1

Abstract

Kanser ölümleri en yüksek oranlı ölüm nedenlerinden biridir. Göğüs kanseri kadınlara özgü olduğu sanılsa da erkeklerde de yaygın olarak görülmekte ve erkeklerde görülen göğüs kanserinde ölüm oranı daha yüksek olabilmektedir. Göğüs kanseri hastalığında erken teşhis ve tedavi çok önemlidir. Uzman sistemler, yapay zekâ ve makine öğrenmesi teknikleri ile kanserin erken evrede teşhisine imkân sağlanmakta ve veri analizleri ile sağlık personellerine kolaylık sunulmaktadır. Bu çalışmada en yakın komşu algoritması, temel bileşen analizi ve komşuluk bileşenleri analizi teknikleri kullanılarak göğüs kanserinin tespiti çalışması gerçekleştirilmiştir. Geliştirilen yöntem “Breast Cancer Wisconsin Diagnostic” veri seti kullanılarak geliştirilmiş ve test edilmiştir. Elde edilen sonuçlara göre en yüksek başarı oranı %99.42 ile komşuluk bileşen analizi ve en yakın komşu sınıflandırma algoritması yöntemi kullanılarak elde edilmiştir.

Keywords

Makine Öğrenimi, Göğüs Kanseri, Wisconsin Veri seti, Sınıflandırma.

Breast Cancer Diagnosis with Machine Learning Techniques

Abstract

Cancer deaths are one of the highest rates of death. Although breast cancer is commonly associated with women, it is sometimes seen in men, and the mortality rate for men with breast cancer may be higher. The importance of early detection and treatment of breast cancer cannot be overstated. Cancer is diagnosed at an early stage thanks to expert systems, artificial intelligence, and machine learning approaches, and data analysis makes life easier for healthcare professionals. The nearest neighbor method, principal component analysis, neighborhood component method approaches were employed to detect breast cancer in this study. "Breast Cancer Wisconsin Diagnostic" database was used to create and test the approach. According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.

Keywords

Machine Learning, Breast Cancer, Wisconsin DataSet, Classification.

References

• 1. World Health Organzation, 2020, International Agency for Research on Cancer-IARC, dowload: https://gco.iarc.fr/today/home.
• 2. Çelik, L., 2020, Meme Kanseri Taramasında Yapay Zeka, download:https://www.drozdogan.com/turkiye-kanser-istatistikleri-2020/
• 3. Eyupoglu, C. (2018). Breast cancer classification using k-nearest neighbors algorithm. The Online Journal of Science and Technology, 8(3), 29-34.
• 4. Jeleń, Ł., Krzyżak, A., Fevens, T., & Jeleń, M. (2016). Influence of feature set reduction on breast cancer malignancy classification of fine needle aspiration biopsies. Computers in Biology and Medicine, 79, 80- 91, doi: 10.1016/j.compbiomed.2016.10.007
• 5. Gupta P., Garg S. (2020). Breast Cancer Prediction using varying Parameters of Machine Learning Models. Procedia Computer Science, vol. 171, pp. 593–601, doi: 10.1016/j.procs.2020.04.064.
• 6. Chaurasia V., Pal S., Tiwari B. (2018). Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology, vol. 12, no. 2, pp. 119–126, doi: 10.1177/1748301818756225.
• 7. Tafish M.H., El-Halees A.M. (2018). Breast Cancer Severity Degree Predication Using Data Mining Techniques in the Gaza Strip,” in 2018 International Conference on Promising Electronic Technologies (ICPET), Deir El-Balah, pp. 124–128, doi: 10.1109/ICPET.2018.00029.
• 8. Gopal V.N., Turjman F.A., Anand L., Rajesh M. (2021). Feature selection and classification in breast cancer prediction using IoT and machine learning. Measurement, 178, 109442, doi: 10.1016/j.measurement.2021.109442
• 9. Sawssen B., Okba T. (2022). A novel machine learning approach for breast cancer diagnosis. Measurement, 187, 110233, doi: 10.1016/j.measurement.2021.110233
• 10. İsmaili F., Shabani L., Raufi B., Adjari J., Zenuni X. (2017). Enhancing breast cancer detection using data mining classification techniques. PressAcademia Procedia, 2nd World Conference on Technology, Innovation and Enterpreunership, 2017, İstanbul, Turkey, doi: 10.17261/Pressacademia.2017.605
• 11. Ateş İ., Bilgin T.T. (2021). The investigation of the success of different machine learning methods in breast cancer diagnosis. Konuralp Medical Journal, 13(2), 347-356, doi: 10.18521/ktd.912462
• 12. Sevli O. (2019). Göğüs kanseri teşhisinde farklı makine öğrenmesi tekniklerinin performans karşılaştırması. Avrupa Bilim ve Teknoloji Dergisi, 16, 176-185, doi: 10.31590/ejosat.553549
• 13. Dua, D., Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
• 14. Zang, A.; Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientist. O’Relly Media Publishing, Sebastopol, USA.
• 15. Salo, F., Nassif, A.B., Essex, A. (2019). Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Computer Networks, 148, 164-175, doi: 10.1016/j.comnet.2018.11.010
• 16. Chiu, H.J., Li, T.H. S., Kuo, P.H. (2020). Breast cancer–detection system using PCA, multilayer perceptron, transfer learning, and support vector machine. IEEE Access, 8, 204309-204324, doi: 10.1109/ACCESS.2020.3036912
• 17. Laghmati, S., Cherradi, B., Tmiri, A., Daanouni, O., Hamida, S. (2020). Classification of Patients with Breast Cancer using Neighbourhood Component Analysis and Supervised Machine Learning Techniques. In 2020 3rd International Conference on Advanced Communication Technologies and Networking (CommNet) (pp. 1-6). IEEE, doi: 10.1109/CommNet49926.2020.9199633
• 18. Khorshid, S.F., Abdulazeez, A.M. (2021). Breast cancer diagnosis based on k-nearest neighbors: A review. PalArch's Journal of Archaeology of Egypt/Egyptology, 18(4), 1927-1951.
• 19. Massafra, R., Latorre, A., Fanizzi, A., Bellotti, R., Didonna, V., Giotta, Lorusso, V. (2021). A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results. Frontiers in Oncology, 11, 284, doi: 10.3389/fonc.2021.576007
• 20. Assegie, T.A. (2021). An optimized K-Nearest Neighbor based breast cancer detection. Journal of Robotics and Control (JRC), 2(3), 115-118, doi: 10.18196/jrc.2363
• 21. Willmott, C.J. and Matsuura, K. (2005). Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE). in Assessing Average Model Performance. Climate Research, 30, 79-82, doi: 10.3354/cr030079