Have a personal or library account? Click to login
Classification methods in the diagnosis of breast cancer Cover

Classification methods in the diagnosis of breast cancer

Open Access
|Dec 2022

References

  1. Agresti A., Kateri M. (2021): Foundations of Statistics for Data Scientists: With R and Python. CRC Press.10.1201/9781003159834
  2. Bennett K.P. (1992): Decision tree construction via linear programming. Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, 97–101.
  3. Bennett K.P., Mangasarian O.L. (1992): Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software 1: 23–34.10.1080/10556789208805504
  4. Breast Cancer Wisconsin Data Set https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+\%28original\%29 [accessed: 21.10.2022].
  5. Breiman L. (2001): Random forests. Machine Learning 45: 5–32.10.1023/A:1010933404324
  6. Chen T., Guestrin C. (2016): XGBoost: A scalable tree boosting system. arXiv: 1603.02754 [accessed: 21.10.2022].10.1145/2939672.2939785
  7. 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: 427.10.1186/1471-2105-11-427293371620716381
  8. 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-x19995703
  9. 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].
  10. Górecki T. (2011): Basics of statistics with examples in R. BTC (in Polish).
  11. James G., Witten D., Hastie T., Tibshirani R. (2017): An Introduction to Statistical Learning with Applications in R. Springer.
  12. 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].
  13. 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.301
  14. 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.2014.11.005434843725750696
  15. Krzyśko M., Wołyński W., Górecki T., Skorzybut M. (2008). Machine learning – pattern recognition, cluster analysis and dimensional reduction. WNT, Warsaw (in Polish).
  16. 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 9739219.10.1155/2021/9739219
  17. 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.
  18. Müller A.C., Guido S. (2018): Introduction to Machine Learning with Python. O'REILLY.
  19. 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).
  20. Saarela M., Jauhiainen S. (2021): Comparison of feature importance measures as explanations for classification models. SN Applied Sciences 3, article number 272.10.1007/s42452-021-04148-9
  21. 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.
  22. 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.
  23. 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.2010.936730422056425382956
  24. 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].
  25. 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-X
  26. VanderPlas J. (2016): Python Data Science Handbook. O'REILLY.
DOI: https://doi.org/10.2478/bile-2022-0008 | Journal eISSN: 2199-577X | Journal ISSN: 1896-3811
Language: English
Page range: 99 - 126
Published on: Dec 30, 2022
Published by: Polish Biometric Society
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2022 Anna Magdalena Ogłoszka, Łukasz Smaga, published by Polish Biometric Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.