BREAST CANCER PREDICTION AND PROGNOSIS USING MACHINE LEARNING

Dooman Maitry; Nikita Pandey; Tushar Sahu

doi:10.59367/5sgxdg77

Authors

Dooman Maitry
Nikita Pandey
Tushar Sahu

DOI:

https://doi.org/10.59367/5sgxdg77

Keywords:

Breast Cancer, Tumor, Chemotherapy, Gene Therapy

Abstract

Nowadays, Breast cancer is the most commonly diagnosed life-threatening cancer in women and the leading root of cancer death among women. In the last two years, research linked to breast cancer has conducted remarkable progression in our understanding of the disease, resulting in more efficient and less toxic treatments. Increased public awareness and improved screening have led to earlier diagnosis at stages amenable to complete surgical resection and curative therapies. Consequently, survival rates for breast cancer have improved significantly, particularly in younger women. This article addresses the types, causes, clinical symptoms, and various approaches both non-drug (such as surgery and radiation) and drug treatment (including chemotherapy, gene therapy, etc.) of breast cancer

References

Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, et al. Global Cancer Observatory: Cancer Today. Lyon: International Agency for Research on Cancer; 2020.

F.J. Shaikh, D.S. Rao, Materials Today: Proceedings “Prediction of Cancer Disease using Machine Learning Approach” 2022.

Andrew J. Vickers Ph.D. : CA Cancer J Clin, “Prediction models in cancer care”; 2011.

NOREEN FATIMA, LI LIU, SHA HONG, AND HAROON AHMED (Student Member, IEEE): “Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis”; 2020.

National Cancer Institute: “Metastatic Cancer: When Cancer Spreads”; 2020.

Yari, Yasin, Hien Nguyen and Thuy V. Nguyen. “Accuracy improvement in binary and multi-class classification of breast histopathology images” 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), IEEE 2021.

T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Mach. Intell. 24 (7) (2002) 971–987.

Joseph A. Cruz, David S. Wishart: “Applications of Machine Learning in Cancer Prediction and Prognosis”; 2006.

Arun Solanki, Fadi Al-Turjman, Meenu Gupta, Rachna Jain: “Cancer Prediction for Industrial IoT 4.0 - Google Books”; 2021.

Y. Uzun and G. Tezel, ‘‘Rule learning with machine learning algorithms and artificial neural networks,’’ J. Seljuk Univ. Natural Appl. Sci., vol. 1, no. 2, pp. 1–11, 2012.

P. Singhal and S. Pareek, ‘‘Artificial neural network for prediction of breast cancer,’’ in Proc. 2nd Int. Conf. I-SMAC (IoT Social, Mobile, Anal. Cloud)(I-SMAC), 2018, pp. 464–468.

H. Sharma and S. Kumar, ‘‘A survey on decision tree algorithms of classification in data mining,’’ Int. J. Sci. Res., vol. 5, no. 4, pp. 2094–2097, 2016.

T. Evgeniou and M. Pontil, ‘‘Support vector machines: Theory and applications,’’ in Advanced Course on Artificial Intelligence. Berlin, Germany: Springer, 2005, pp. 249–257.

Milecia McGregor: “SVM Machine Learning Tutorial – What is the Support Vector Machine Algorithm, Explained with Code Examples”;2020

H. Tran, ‘‘A survey of machine learning and data mining techniques used in multimedia system,’’ Dept. Comput. Sci., Univ. Texas Dallas Richardson, Richardson, TX, USA, Tech. Rep., Sep. 2019.

Y. Yang, J. Li, and Y. Yang, ‘‘The research of fast SVM classifier method,’’ in Proc. 12th Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process. (ICCWAMTIP), Dec. 2015, pp. 121–124.

T. O. Ayodele, ‘‘Types of machine learning algorithms,’’ New Adv. Mach. Learn., vol. 3, pp. 19–48, Feb. 2010.

Y. Li and H. Wu, ‘‘A clustering method based on K-means algorithm,’’ Phys. Procedia, vol. 25, pp. 1104–1109, Jan. 2012.

Hiba Asri, Hajar Mousannif, Hassan Al Moatassime, Thomas Noel: “Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis”; 2016

K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34]