Machine learning techniques in cancer prognostic modeling and performance assessment

Yiyi Chen, Jess A. Millar

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations


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 discrimination and calibration 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.

Original languageEnglish (US)
Title of host publicationFrontiers of Biostatistical Methods and Applications in Clinical Oncology
PublisherSpringer Singapore
Number of pages38
ISBN (Electronic)9789811001260
ISBN (Print)9789811001246
StatePublished - Oct 3 2017


  • Cancer prediction
  • Machine learning
  • Prognostic model
  • Validation

ASJC Scopus subject areas

  • General Medicine
  • General Mathematics
  • General Social Sciences


Dive into the research topics of 'Machine learning techniques in cancer prognostic modeling and performance assessment'. Together they form a unique fingerprint.

Cite this