IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v38y2018i7p822-833.html
   My bibliography  Save this article

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

Author

Listed:
  • Annemieke Witteveen
  • Gabriela F. Nane
  • Ingrid M.H. Vliegen
  • Sabine Siesling
  • Maarten J. IJzerman

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 different cutoffs, 3) constraint-based learning algorithms, and 4) score-based learning algorithms. The different models were compared with logistic regression using the area under the receiver operating characteristic curve, an external validation set obtained from the NCR from 2007 and 2008 ( N = 12,308), and subgroup analyses for a high- and low-risk group. Results . The BNs with the most links showed the best performance in both LRR and SP prediction (c-statistic of 0.76 for LRR and 0.69 for SP). In the external validation, logistic regression generally outperformed the BNs in both SP and LRR (c-statistic of 0.71 for LRR and 0.64 for SP). The differences were nonetheless small. Although logistic regression performed best on most parts of the subgroup analysis, BNs outperformed regression with respect to average risk for SP prediction in low- and high-risk groups. Conclusions . Although estimates of regression coefficients depend on other independent variables, there is no assumed dependence relationship between coefficient estimators and the change in value of other variables as in the case of BNs. Nonetheless, this analysis suggests that regression is still more accurate or at least as accurate as BNs for risk estimation for both LRRs and SP tumors.

Suggested Citation

  • Annemieke Witteveen & Gabriela F. Nane & Ingrid M.H. Vliegen & Sabine Siesling & Maarten J. IJzerman, 2018. "Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence," Medical Decision Making, , vol. 38(7), pages 822-833, October.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:7:p:822-833
    DOI: 10.1177/0272989X18790963
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X18790963
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X18790963?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. M Berkan Sesen & Ann E Nicholson & Rene Banares-Alcantara & Timor Kadir & Michael Brady, 2013. "Bayesian Networks for Clinical Decision Support in Lung Cancer Care," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zsolt Zador & Matthew Sperrin & Andrew T King, 2016. "Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    2. Catarina Moreira & Emmanuel Haven & Sandro Sozzo & Andreas Wichert, 2018. "Process mining with real world financial loan applications: Improving inference on incomplete event logs," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-31, December.
    3. Mroczek Teresa & Skica Tomasz & Rodzinka Jacek, 2019. "Optimal Size of the General Government Sector from the Point of View of its Impact on the EU Economies," South East European Journal of Economics and Business, Sciendo, vol. 14(2), pages 95-105, December.
    4. Mroczek Teresa & Skica Tomasz & Rodzinka Jacek, 2018. "Application of Probabilistic Inference in Defining Impact of the General Government Sector’s Size on the Economy and Determining the Size of the Sector by the Economy in the EU," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(1), pages 1-11, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:38:y:2018:i:7:p:822-833. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.