Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study.

ABSTRACT:

Background

Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening.

Methods and findings

For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002

SUBMITTER: Callender T 

PROVIDER: S-EPMC10547178 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature