Breast cancer recurrence prediction using machine learning

Kaustubh Chakradeo; Sanyog Vyawahare; Pranav Pawar

doi:10.1109/cict48419.2019.9066248

Breast cancer recurrence prediction using machine learning

Kaustubh Chakradeo, Sanyog Vyawahare, Pranav Pawar

Bar-Ilan University - The Alexander Kofkin Faculty of Engineering

University of Pune

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

The most common cancer among women is breast cancer. Around 12% of women are affected by it all over the world. Recurrent breast cancer is a term used for breast cancer which returns even after a successful treatment. This research aims to use Machine learning to detect and predict the recurrence of breast cancer; and compare all the models by using different metrics like accuracy, precision, etc. The models built can help predict the recurrence of breast cancer effectively. All the models are built using the Wisconsin Prognostic Breast Cancer Dataset(WPBC). The models built are Multiple Linear Regression, Support Vector Machine, which was build by using RBF Kernel and Leave-One-Out(K-fold Cross-Validation) and Decision Tree using metrics like Gini Index, Entropy and Information Gain. Support Vector Machine and K-fold Cross-Validation gave the best results for recurrence and non-recurrence predictions.

Original language	English
Title of host publication	2019 IEEE Conference on Information and Communication Technology, CICT 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728153988
DOIs	https://doi.org/10.1109/cict48419.2019.9066248
State	Published - Dec 2019
Event	2019 IEEE Conference on Information and Communication Technology, CICT 2019 - Allahabad, India Duration: 6 Dec 2019 → 8 Dec 2019

Publication series

Name	2019 IEEE Conference on Information and Communication Technology, CICT 2019

Conference

Conference	2019 IEEE Conference on Information and Communication Technology, CICT 2019
Country/Territory	India
City	Allahabad
Period	6/12/19 → 8/12/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Breast Cancer
Decision Tree
Prognosis
Recurrence
Regression
SVM

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/cict48419.2019.9066248

Cite this

Chakradeo, K., Vyawahare, S., & Pawar, P. (2019). Breast cancer recurrence prediction using machine learning. In 2019 IEEE Conference on Information and Communication Technology, CICT 2019 Article 9066248 (2019 IEEE Conference on Information and Communication Technology, CICT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/cict48419.2019.9066248

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abstract = "The most common cancer among women is breast cancer. Around 12% of women are affected by it all over the world. Recurrent breast cancer is a term used for breast cancer which returns even after a successful treatment. This research aims to use Machine learning to detect and predict the recurrence of breast cancer; and compare all the models by using different metrics like accuracy, precision, etc. The models built can help predict the recurrence of breast cancer effectively. All the models are built using the Wisconsin Prognostic Breast Cancer Dataset(WPBC). The models built are Multiple Linear Regression, Support Vector Machine, which was build by using RBF Kernel and Leave-One-Out(K-fold Cross-Validation) and Decision Tree using metrics like Gini Index, Entropy and Information Gain. Support Vector Machine and K-fold Cross-Validation gave the best results for recurrence and non-recurrence predictions.",

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Chakradeo, K, Vyawahare, S & Pawar, P 2019, Breast cancer recurrence prediction using machine learning. in 2019 IEEE Conference on Information and Communication Technology, CICT 2019., 9066248, 2019 IEEE Conference on Information and Communication Technology, CICT 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Conference on Information and Communication Technology, CICT 2019, Allahabad, India, 6/12/19. https://doi.org/10.1109/cict48419.2019.9066248

Breast cancer recurrence prediction using machine learning. / Chakradeo, Kaustubh; Vyawahare, Sanyog; Pawar, Pranav.
2019 IEEE Conference on Information and Communication Technology, CICT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 9066248 (2019 IEEE Conference on Information and Communication Technology, CICT 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Breast cancer recurrence prediction using machine learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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