Breast tumor prediction and feature importance score finding using machine learning algorithms
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Definition of tumor, NCI Dictionary of Cancer Terms. Available at: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/tumor (accessed: Feb. 23, 2023).
What Is Cancer? Available at: https://www.cancer.gov/about-cancer/understanding/what-is-cancer/ (accessed October 11, 2021).
Testa, U., Castelli, G., & Pelosi, E. Breast cancer: a molecularly heterogenous disease needing subtype-specific treatments. Medical Sciences, 2020, vol. 8, no. 1, article no. 18. DOI: 10.3390/medsci8010018.
Breast Cancer Facts and Statistics. Available at: https://www.breastcancer.org/facts-statistics (Accessed on Jan. 19, 2023).
Gayathri, B. M., Sumathi, C. P., & Santhanam, T. Breast cancer diagnosis using machine learning algorithms – a survey. International Journal of Distributed and Parallel Systems (IJDPS), 2013, vol. 4, iss. 3, pp. 105-112. DOI: 10.5121/ijdps.2013.4309.
Nemade, V., Pathak, S., & Dubey, A. K. A systematic literature review of breast cancer diagnosis using machine intelligence techniques. Archives of Computational Methods in Engineering, 2022, vol. 29, no. 6, pp. 4401-4430. DOI: 10.1007/s11831-022-09738-3.
Elsadig, M. A., Altigani, A., & Elshoush, H. T. Breast cancer detection using machine learning approaches: a comparative study. International Journal of Electrical & Computer Engineering, 2023, vol. 13, no. 1, pp. 736-745. DOI: 10.11591/ijece.v13i1.pp736-745.
Mangasarian, O. L., & Wolberg, W. H. Cancer diagnosis via linear programming. University of Wisconsin-Madison. Computer Sciences Department, 1990. 5 p. Available at: http://digital.library.wisc.edu/1793/59346. (Accessed on Dec. 23, 2022).
Lee, H., Yoon, T. J., Figueiredo, J. L., Swirski, F. K., & Weissleder, R. Rapid detection and profiling of cancer cells in fine-needle aspirates. Proceedings of the National Academy of Sciences, 2009, vol. 106, no. 30, pp. 12459-12464. DOI: 10.1073/pnas.0902365106.
Ara, S., Das, A., & Dey, A. Malignant and benign breast cancer classification using machine learning algorithms. In 2021 International Conference on Artificial Intelligence (ICAI), Islamabad, Pakistan, 2022, pp. 97-101. DOI: 10.1109/ICAI52203.2021.9445249.
Chaurasia, V., Pal, S., & Tiwari, B. B. Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology, 2018, vol. 12, no. 2, pp. 119-126. DOI: 10.1177/1748301818756225.
Li, Y., & Chen, Z. Performance evaluation of machine learning methods for breast cancer prediction. Appl Comput Math, 2018, vol. 7, no. 4, pp. 212-216. DOI: 10.11648/j.acm.20180704.15.
Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., & Caramelo, F. Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer, 2018, vol. 18, no. 1, article no. 29, pp. 1-8. DOI: 10.1186/s12885-017-3877-1.
Asri, H., Mousannif, H., Al Moatassime, H., & Noel, T. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Science, 2016, vol. 83, pp. 1064-1069. DOI: 10.1016/j.procs.2016.04.224.
Wolberg, W. Breast Cancer Wisconsin (Original). Dataset. UCI Machine Learning Repository, 1992. DOI: 10.24432/C5HP4Z.
Kurn, H., & Daly, D. T. Histology, epithelial cell, StatPearls - NCBI BookShelf. Available at: https://www.ncbi.nlm.nih.gov/books/NBK559063/ (Accessed on Feb. 17, 2023).
What is ffill and bfill in pandas? Available at: https://www.projectpro.io/recipes/what-is-ffill-and-bfill-pandas (Accessed on Dec. 23, 2022).
Kumar, S., & Chong, I. Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states. International journal of environmental research and public health, 2018, vol. 15, no. 12, article no. 2907. DOI: 10.3390/ijerph15122907.
Pothuganti, S. Review on over-fitting and under-fitting problems in Machine Learning and solutions. Int. J. Adv. Res. Electr. Electron. Instrumentation Eng, 2018 vol. 7, no. 9, pp. 3692-3695. Available at: http://www.ijareeie.com/upload/2018/september/11A_PS_NC.PDF. (Accessed on Feb. 17, 2023). DOI: 10.15662/IJAREEIE.2018.0709015.
Montesinos López, O. A., Montesinos López, A., & Crossa, J. Overfitting, Model Tuning, and Evaluation of Prediction Performance. In Multivariate statistical machine learning methods for genomic prediction, 2022, pp. 109-139. Cham: Springer International Publishing. DOI: 10.1007/978-3-030-89010-0_4.
Martyniuk, T., Krukivskyi, B., Kupershtein, L., & Lukichov, V. Neural Network model of heteroassociative memory for the classification task. Radioelectronic and Computer Systems, 2022, vol. 2, pp. 108-117. DOI: 10.32620/reks.2022.2.09.
Krivtsov, S., Meniailov, I., Bazilevych, K., & Chumachenko, D. Predictive model of COVID-19 epidemic process based on neural network. Radioelectronic and Computer Systems, 2022, vol. 4, pp. 7-18. DOI: 10.32620/reks.2022.4.01.
Tarle, B., & Akkalaksmi, M., Improving classification performance of neuro fuzzy classifier by imputing missing data. International Journal of Computing, 2019, vol. 18, iss. 4, pp. 495-501. DOI: 10.47839/ijc.18.4.1619.
Striuk, O., & Kondratenko, Yu. Generative adversarial neural networks and deep learning: successful cases and advanced approaches. International Journal of Computing, 2021, vol. 20, iss. 3, pp. 339-349. DOI: 10.47839/ijc.20.3.2278.
DOI: https://doi.org/10.32620/reks.2023.4.03
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