Prescription data and demographics: An explainable machine learning exploration of colorectal cancer risk factors based on data from Danish national registries
Machine learning models can identify individuals at higher risk for developing colorectal cancer, enabling earlier interventions and personalized risk management strategies. However, further studies are needed before implementation in clinical practice.
Authors
- Søren P. Sheikh
- Bente Mertz Nørgård (ORCID: https://orcid.org/0000-0002-9514-3033)
- Dorte Ejg Jarbøl (ORCID: https://orcid.org/0000-0002-3241-4190)
- Jens Søndergaard (ORCID: https://orcid.org/0000-0002-1629-1864)
- Ali Ebrahimi (ORCID: https://orcid.org/0000-0002-3332-6205)
- Maria Iachina (ORCID: https://orcid.org/0000-0001-5319-9285)
- Sonja Wehberg (ORCID: https://orcid.org/0000-0003-1503-2649)
- Olav Sivertsen Garvik (ORCID: https://orcid.org/0000-0002-6062-1488)
- Abdolrahman Peimankar (ORCID: https://orcid.org/0000-0002-3180-4365)
- Uffe Kock Wiil (ORCID: https://orcid.org/0000-0001-6898-4083)
Institutions
- University of Southern Denmark (DK)
- Maersk (Denmark) (DK)
- Odense University Hospital (DK)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108774
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00