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Machine Learning Techniques in Cancer Prognostic Modeling and Performance Assessment

In: Frontiers of Biostatistical Methods and Applications in Clinical Oncology

Author

Listed:
  • Yiyi Chen

    (Oregon Health & Science University, OHSU-PSU School of Public Health, Knight Cancer Institute)

  • Jess A. Millar

    (Portland State University, Fariborz Maseeh Department of Mathematics and Statistics)

Abstract

Prognostic models for disease occurrence, tumor progression and survival are abundant for most types of cancers. Physicians and cancer patients are utilizing these models to make informed treatment decisions and corresponding arrangements. However, not all cancer prognostic models are built and validated rigorously. Some are more useful and reliable than others. In this chapter, we briefly introduce some popular machine learning methods for constructing cancer prognostic models, and discuss pros and cons of each. We also introduce the commonly used discriminationDiscrimination and calibrationCalibration metrics for assessing predictive performance and validating the prognostic models. In the end, we outline several challenges of using prognostic models in the real world for clinical decision-making support, and propose related suggestions.

Suggested Citation

  • Yiyi Chen & Jess A. Millar, 2017. "Machine Learning Techniques in Cancer Prognostic Modeling and Performance Assessment," Springer Books, in: Shigeyuki Matsui & John Crowley (ed.), Frontiers of Biostatistical Methods and Applications in Clinical Oncology, pages 193-230, Springer.
    • Handle: RePEc:spr:sprchp:978-981-10-0126-0_13
    DOI: 10.1007/978-981-10-0126-0_13
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