A Population-Specific Ensemble Machine Learning Model for Predicting Borderline or Malignancy Risk of Ovarian Masses in Macao: A Multicenter Retrospective Study
We propose a machine learning model targeting Macao's population for predicting the borderline or malignancy risk of ovarian masses. Our model is accurate, low-cost, easily accessible, and interpretable. On the basis of no workflow changes, machine learning techniques can maximize the predictive potential of routinely available clinical data in a specific health system.
Authors
- I‐San Chan (ORCID: https://orcid.org/0000-0002-6074-5915)
- K. Arnold Chan (ORCID: https://orcid.org/0000-0001-8161-1986)
- Shaodong Hong (ORCID: https://orcid.org/0000-0002-5632-6857)
- Sio‐In Wong
- Chan-Fong Chio (ORCID: https://orcid.org/0000-0001-9804-2361)
- Lai-Fong Sin
- Hoi-Sun Loi
- Hou-Kong Cheang
- Wai-Ieng Fong
Institutions
- Kiang Wu Hospital (CN)
- Hospital Conde S. Januário (CN)
Publication Details
- Journal
- Clinical Medicine Insights Oncology
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1177/11795549251388312
- Primary Topic
- Ovarian cancer diagnosis and treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministerio de Economía y Competitividad