Classification methods in the diagnosis of breast cancer

Agresti A., Kateri M. (2021): Foundations of Statistics for Data Scientists: With R and Python. CRC Press.10.1201/9781003159834Search in Google Scholar

Bennett K.P. (1992): Decision tree construction via linear programming. Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, 97–101.Search in Google Scholar

Bennett K.P., Mangasarian O.L. (1992): Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software 1: 23–34.10.1080/10556789208805504Search in Google Scholar

Breast Cancer Wisconsin Data Set https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+\%28original\%29 [accessed: 21.10.2022].Search in Google Scholar

Breiman L. (2001): Random forests. Machine Learning 45: 5–32.10.1023/A:1010933404324Search in Google Scholar

Chen T., Guestrin C. (2016): XGBoost: A scalable tree boosting system. arXiv: 1603.02754 [accessed: 21.10.2022].10.1145/2939672.2939785Search in Google Scholar

Datta S., Pihur V., Datta S. (2010): An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data. BMC Bioinformatics 11: 427.10.1186/1471-2105-11-427293371620716381Search in Google Scholar

Diamantis A., Magiorkinis E., Koutselini H. (2009): Fine-needle aspiration (FNA) biopsy: historical aspects. Folia Histochemica Et Cytobiologica 47: 191–197.10.2478/v10042-009-0027-x19995703Search in Google Scholar

Dua D., Graff C. (2019): UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science http://archive.ics.uci.edu/ml [accessed: 21.10.2022].Search in Google Scholar

Górecki T. (2011): Basics of statistics with examples in R. BTC (in Polish).Search in Google Scholar

James G., Witten D., Hastie T., Tibshirani R. (2017): An Introduction to Statistical Learning with Applications in R. Springer.Search in Google Scholar

Jaworski B. (2015): Cost-optimal sampling of examples for imbalanced data. Master's thesis defended in Computer Science. Poznań University of Technology, Poznań (in Polish) https://kofeina.net/~benek/studia/praca_magisterska_benedykt_jaworski.pdf [accessed: 21.10.2022].Search in Google Scholar

Kaelbling L.P., Littman M.L., Moore A.W. (1996): Reinforcement learning: A Survey. Journal of Artificial Intelligence Research 4: 237–285.10.1613/jair.301Search in Google Scholar

Kourou K., Exarchos T.P., Exarchos K.P., Karamouzis M.V., Fotiadis D.I. (2015): Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal 13: 8–17.10.1016/j.csbj.2014.11.005434843725750696Search in Google Scholar

Krzyśko M., Wołyński W., Górecki T., Skorzybut M. (2008). Machine learning – pattern recognition, cluster analysis and dimensional reduction. WNT, Warsaw (in Polish).Search in Google Scholar

Li Y., Shan B., Li B., Liu X., Pu Y. (2021): Literature review on the applications of machine learning and blockchain technology in smart healthcare industry: A bibliometric analysis. Journal of Healthcare Engineering 9739219.10.1155/2021/9739219Search in Google Scholar

Mezouar H., Afia A.E. (2022): A systematic literature review of machine learning applications in software engineering. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham.Search in Google Scholar

Müller A.C., Guido S. (2018): Introduction to Machine Learning with Python. O'REILLY.Search in Google Scholar

Ogłoszka A.M. (2022): Classification methods in the diagnosis of breast cancer. Master's thesis defended in Data Science. Adam Mickiewicz University, Poznań (in Polish).Search in Google Scholar

Saarela M., Jauhiainen S. (2021): Comparison of feature importance measures as explanations for classification models. SN Applied Sciences 3, article number 272.10.1007/s42452-021-04148-9Search in Google Scholar

Salama G.I., Abdelhalim M.B., Zeid M. (2012): Breast cancer diagnosis on three different datasets using multi-classifiers. International Journal of Computer and Information Technology 1: 36–43.Search in Google Scholar

Singh P., Singh S.P., Singh D.S. (2019): An introduction and review on machine learning applications in medicine and healthcare. 2019 IEEE Conference on Information and Communication Technology, pages 1–6, doi: 10.1109/CICT48419.2019.9066250.Open DOISearch in Google Scholar

Wernick M.N., Yang Y., Brankov J.G., Yourganov G., Strother S.C. (2010): Machine learning in medical imaging. IEEE Signal Processing Magazine 27: 25–38.10.1109/MSP.2010.936730422056425382956Search in Google Scholar

Wojciechowska U., Didkowska J. (2013): Illnesses and deaths from malignant neoplasms in Poland. National Cancer Registry from National Oncology Institute Maria Skłodowska-Curie – National Research Institute. Available at http://onkologia.org.pl/raporty/ [accessed on 06.01.2022].Search in Google Scholar

Wolberg W.H., Street W.N., Mangasarian O.L. (1994): Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77: 163–171.10.1016/0304-3835(94)90099-XSearch in Google Scholar

VanderPlas J. (2016): Python Data Science Handbook. O'REILLY.Search in Google Scholar