ORCID
- Zhou, Shang-Ming: 0000-0002-0719-9353
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 CHARM checklist against which the models were benchmarked. The review found similar methodological problems with machine-learning models to that observed in systematic reviews for non-machine-learning models, although model performance was better. The inclusion of machine-learning models in systematic reviews is required, as they offer improved performance despite similar methodological omissions; however, to achieve this the methodological issues that affect many prediction models need to be addressed.
DOI
10.3390/diagnostics13020301
Publication Date
2023-01-13
Publication Title
Diagnostics
Volume
13
Issue
2
Embargo Period
2023-02-14
Organisational Unit
School of Nursing and Midwifery
Recommended Citation
Burnett, B., Zhou, S., Brophy, S., Davies, P., Ellis, P., Kennedy, J., Bandyopadhyay, A., Parker, M., & Lyons, R. (2023) 'Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review', Diagnostics, 13(2). Available at: https://doi.org/10.3390/diagnostics13020301
