Journal article

Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence

Annemieke Witteveen, Gabriela F Nane, Ingrid MH Vliegen, Sabine Siesling, Maarten J IJzerman

MEDICAL DECISION MAKING | SAGE PUBLICATIONS INC | Published : 2018

Abstract

PURPOSE: For individualized follow-up, accurate prediction of locoregional recurrence (LRR) and second primary (SP) breast cancer risk is required. Current prediction models employ regression, but with large data sets, machine-learning techniques such as Bayesian Networks (BNs) may be better alternatives. In this study, logistic regression was compared with different BNs, built with network classifiers and constraint- and score-based algorithms. METHODS: Women diagnosed with early breast cancer between 2003 and 2006 were selected from the Netherlands Cancer Registry (NCR) ( N = 37,320). BN structures were developed using 1) Bayesian network classifiers, 2) correlation coefficients with diffe..

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