Machine learning explainability in breast cancer survival

Tom Jansen, Gijs Geleijnse, Marissa van Maaren, Mathijs P. Hendriks, Annette Ten Teije, Arturo Moncada-Torres*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

23 Citations (Scopus)
176 Downloads (Pure)


Machine Learning (ML) can improve the diagnosis, treatment decisions, and understanding of cancer. However, the low explainability of how “black box” ML methods produce their output hinders their clinical adoption. In this paper, we used data from the Netherlands Cancer Registry to generate a ML-based model to predict 10-year overall survival of breast cancer patients. Then, we used Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to interpret the model's predictions. We found that, overall, LIME and SHAP tend to be consistent when explaining the contribution of different features. Nevertheless, the feature ranges where they have a mismatch can also be of interest, since they can help us identifying “turning points” where features go from favoring survived to favoring deceased (or vice versa). Explainability techniques can pave the way for better acceptance of ML techniques. However, their evaluation and translation to real-life scenarios need to be researched further.

Original languageEnglish
Title of host publicationDigital Personalized Health and Medicine - Proceedings of MIE 2020
EditorsLouise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
Number of pages5
ISBN (Electronic)9781643680828
Publication statusPublished - 16 Jun 2020
Event30th Medical Informatics Europe Conference, MIE 2020 - Canceled, Geneva, Switzerland
Duration: 28 Apr 20201 May 2020
Conference number: 30

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference30th Medical Informatics Europe Conference, MIE 2020
Abbreviated titleMIE 2020


  • Artificial Intelligence
  • Interpretability
  • Oncology
  • Prediction model


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