Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review.

ABSTRACT: The inclusion of machine-learning-derived models in systematic reviews of risk prediction models for colorectal cancer is rare. Whilst such reviews have highlighted methodological issues and limited performance of the models included, it is unclear why machine-learning-derived models are absent and whether such models suffer similar methodological problems. This scoping review aims to identify machine-learning models, assess their methodology, and compare their performance with that found in previous reviews. A literature search of four databases was performed for colorectal cancer prediction and prognosis model publications that included at least one machine-learning model. A total of 14 publications were identified for inclusion in the scoping review. Data was extracted using an adapted

SUBMITTER: Burnett B 

PROVIDER: S-EPMC9858109 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature