Öz
Amaç: Bu çalışmanın amacı, makine öğrenimi yöntemlerini kullanarak kanseri yaşamın erken dönemlerinde belirlemektir.
Gereç ve Yöntem: Bu amaçla, Wisconsin Diagnostic Breast Cancer veri setinin Naive Bayes, karar ağaçları, yapay sinir ağları ile sınıflandırılması yapılmış ve söz konusu makine öğrenme yöntemleri karşılaştırılmıştır. Uygulamalar için ‘KNIME Analytics Platform’ u kullanılmıştır. Sınıflandırma işlemi yapılmadan önce veri seti ön işlemeden geçirilmiştir. Ön işleme aşamasından sonra, verilere üç farklı sınıflandırıcı yöntem uygulanmıştır. Yöntemlerin başarısını ölçmek için doğruluk, duyarlılık, özgüllük, hata matrisleri ve ROC eğrileri kullanılmıştır.
Bulgular: Uygulama sonuçları, Naive Bayes ve yapay sinir ağı yöntemlerinin tümörleri %96.5 doğrulukla doğru olarak sınıflandırdığını göstermektedir. Karar ağacı yönteminin sınıflandırmadaki başarısı %92.6 olarak elde edilmiştir. Sonuç olarak, her üç modelin üstün doğruluğa sahip sınıflandırma yaptığı söylenebilir.
Sonuç: Makine öğrenme algoritmaları, meme kanseri teşhisinde tümörlerin kötü huylu veya iyi huylu olup olmadığını belirlemek için başarıyla kullanılabilir.
Anahtar Kelimeler
Meme kanseri teşhisi Makine öğrenmesi Naive Bayes Karar ağaçları Yapay sinir ağları KNIME
Kaynakça
- https://gco.iarc.fr/today/data/factsheets/cancers/20-Breast-fact-sheet.pdf. Erişim Tarihi: 11.01.2021.
- https://gco.iarc.fr/today/data/factsheets/populations/792-turkey-fact-sheets.pdf. Erişim Tarihi: 11.01.2021.
- Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 36(2), 3240-3247.
- Gayathri, B. M., Sumathi, C. P. and Santhanam, T. (2013). Breast Cancer Diagnosis Using Machine Learning Algorithms - A Survey. International Journal of Distributed and Parallel Systems, 4(3), 105–112.
- Yue, W., Wang, Z., Chen, H., Payne, A., and Liu, X. (2018). Machine learning with applications in breast cancer diagnosis and prognosis. Designs, 2(2), 13. 2(2), 13.
- Agarap, A. F. M. (2018). On breast cancer detection. Proceedings of the 2nd International Conference on Machine Learning and Soft Computing.
- Rodrigues, B. L. (2015). Analysis of the Wisconsin Breast Cancer dataset and machine learning for breast cancer detection. In: Proceedings of XI Workshop de Visão Computational, 15–19.
- Asri, H., Mousannif, H., Moatassime, H. A. and Noel, T. (2016). Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis. Procedia Computer Science, 83, 1064–1069.
- Saygılı, A. (2018). Classification and Diagnostic Prediction of Breast Cancers via Different Classifiers. International Scientific and Vocational Journal, 2(2), 45-56.
- Basci̇ftci̇, F. and Ünal, H. T. (2019). An Empirical Comparison of Machine Learning Algorithms for Predicting Breast Cancer. Bilge International Journal of Science and Technology Research, ICONST 2019 , 9-20.
- Mohammed S.A., Darrab S., Noaman S.A. and Saake G. (2020). Analysis of Breast Cancer Detection Using Different Machine Learning Techniques. In: Tan Y., Shi Y., Tuba M. (eds) Data Mining and Big Data. DMBD 2020. Communications in Computer and Information Science, vol 1234. Springer, Singapore.
- Wolberg, W. H., Street W. N. and Mangasarian, O. L. (1992). Breast cancer Wisconsin (diagnostic) data set. UCI Machine Learning Repository. http://archive. ics. uci. edu/ml/.
- https://www.kaggle.com/uciml/breast-cancer-wisconsin-data. Erişim Tarihi: 11.01.2021.
Öz
Objective: The aim of this study is to identify cancer earlier in life using machine learning methods.
Methods: For this purpose, the Wisconsin Diagnostic Breast Cancer dataset was classified using Naive Bayes, decision trees, artificial neural networks algorithms and comparison of these machine learning methods was made. KNIME Analytics Platform was used for applications. Before the classification process, the dataset was preprocessed. After the pre-processing stage, three different classifier methods were applied to the dataset. Accuracy, sensitivity, specificity and confusion matrices were used to measure the success of the methods.
Results: The results show that Naive Bayes and artificial neural network methods classify tumors with 96.5% accuracy. The success of the decision tree method in classification was 92.6%.
Conclusion: The machine learning algorithms can be used successfully in breast cancer diagnosis to determine whether the tumors are malign or benign.
Anahtar Kelimeler
Breast Cancer Diagnosis Machine Learning Naive Bayes Decision Trees Artificial Neural Networks
Kaynakça
- https://gco.iarc.fr/today/data/factsheets/cancers/20-Breast-fact-sheet.pdf. Erişim Tarihi: 11.01.2021.
- https://gco.iarc.fr/today/data/factsheets/populations/792-turkey-fact-sheets.pdf. Erişim Tarihi: 11.01.2021.
- Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 36(2), 3240-3247.
- Gayathri, B. M., Sumathi, C. P. and Santhanam, T. (2013). Breast Cancer Diagnosis Using Machine Learning Algorithms - A Survey. International Journal of Distributed and Parallel Systems, 4(3), 105–112.
- Yue, W., Wang, Z., Chen, H., Payne, A., and Liu, X. (2018). Machine learning with applications in breast cancer diagnosis and prognosis. Designs, 2(2), 13. 2(2), 13.
- Agarap, A. F. M. (2018). On breast cancer detection. Proceedings of the 2nd International Conference on Machine Learning and Soft Computing.
- Rodrigues, B. L. (2015). Analysis of the Wisconsin Breast Cancer dataset and machine learning for breast cancer detection. In: Proceedings of XI Workshop de Visão Computational, 15–19.
- Asri, H., Mousannif, H., Moatassime, H. A. and Noel, T. (2016). Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis. Procedia Computer Science, 83, 1064–1069.
- Saygılı, A. (2018). Classification and Diagnostic Prediction of Breast Cancers via Different Classifiers. International Scientific and Vocational Journal, 2(2), 45-56.
- Basci̇ftci̇, F. and Ünal, H. T. (2019). An Empirical Comparison of Machine Learning Algorithms for Predicting Breast Cancer. Bilge International Journal of Science and Technology Research, ICONST 2019 , 9-20.
- Mohammed S.A., Darrab S., Noaman S.A. and Saake G. (2020). Analysis of Breast Cancer Detection Using Different Machine Learning Techniques. In: Tan Y., Shi Y., Tuba M. (eds) Data Mining and Big Data. DMBD 2020. Communications in Computer and Information Science, vol 1234. Springer, Singapore.
- Wolberg, W. H., Street W. N. and Mangasarian, O. L. (1992). Breast cancer Wisconsin (diagnostic) data set. UCI Machine Learning Repository. http://archive. ics. uci. edu/ml/.
- https://www.kaggle.com/uciml/breast-cancer-wisconsin-data. Erişim Tarihi: 11.01.2021.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Sağlık Kurumları Yönetimi |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 7 Haziran 2021 |
| Kabul Tarihi | 2 Haziran 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 13 Sayı: 2 |
