Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Prediction of Breast Cancer Recurrence With Machine Learning

Prediction of Breast Cancer Recurrence With Machine Learning
View Sample PDF
Author(s): Mohammad Mehdi Owrang O. (American University, USA), Ginger Schwarz (American University, USA)and Fariba Jafari Horestani (American University, USA)
Copyright: 2025
Pages: 33
Source title: Encyclopedia of Information Science and Technology, Sixth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/978-1-6684-7366-5.ch061


View Prediction of Breast Cancer Recurrence With Machine Learning on the publisher's website for pricing and purchasing information.


Medical prognostication is the science of estimating the complication and recurrence of a disease. A Breast cancer recurrence (BCR) event is characterized by the cancer “coming back” after at least a year of remission after the treatment. Many factors, including tumor grade, tumor size, and lymph node status may influence or correlate with prognosis for breast cancer patients. Early detection of recurrence events (i.e., while still asymptomatic) is more likely to be curable than after the cancer symptoms are seen again. Machine learning techniques can help to provide some necessary information and knowledge required by physicians for accurate predictions of BCR and better decision-making. The aim of this chapter is to use machine learning classifiers to examine the factors that are most predictive of the BCR. Several attributes/features selection schemes have been used to find the most significant features contributing to BCR. Five different machine learning algorithms were tested and compared for the prediction of BCR. The decision tree was found to be the best model for the dataset.

Related Content

Christian Rainero, Giuseppe Modarelli. © 2025. 26 pages.
Beatriz Maria Simões Ramos da Silva, Vicente Aguilar Nepomuceno de Oliveira, Jorge Magalhães. © 2025. 21 pages.
Ann Armstrong, Albert J. Gale. © 2025. 19 pages.
Zhi Quan, Yueyi Zhang. © 2025. 21 pages.
Sanaz Adibian. © 2025. 19 pages.
Le Ngoc Quang, Kulthida Tuamsuk. © 2025. 21 pages.
Jorge Lima de Magalhães, Carla Cristina de Freitas da Silveira, Tatiana Aragão Figueiredo, Felipe Gilio Guzzo. © 2025. 17 pages.
Body Bottom