Machine learning for predicting breast cancer risk and outcomes: a scoping review
https://doi.org/10.13181/mji.rev.268261
- Author:
Dini Alyani
1
;
Soehartati Gondhowiardjo
1
Author Information
1. Department of Radiation Oncology, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
- Publication Type:Review
- Keywords:
breast neoplasm;
machine learning;
predictive value of tests
- MeSH:
breast neoplasm;
machine learning;
predictive value of tests
- From:
Medical Journal of Indonesia
2026;35(2):151-162
- CountryIndonesia
- Language:English
-
Abstract:
Breast cancer (BC) is the most frequently diagnosed malignancy worldwide and a leading cause of death in women. Machine learning (ML) enables data-driven risk stratification and outcome prediction, potentially improving screening, treatment choices, and overall care. This scoping review cataloged ML models used to predict BC risk and post-treatment outcomes. A PubMed search using MeSH terms (“breast neoplasms,” “breast cancer,” “machine learning,” and “predictive value of tests”) retrieved 44 records, with 9 added by manual searching; 24 studies were included. We excluded 29 reports limited to imaging/radiomics or biomolecular/omics, plus other ineligible items. Model usage among included studies: 13 random forest (RF); 13 support vector machine; 7 decision tree; 5 k-nearest neighbors; 4 deep learning; 3 naïve Bayes; 2 artificial neural networks. Average RF accuracy surpassed 90% in most reports, indicating RF is both common and consistently strong for predictive modeling, supporting targeted clinical translation.
- Full text:202610211117358387535-2-8261-Dini-Alyani_xmp.pdf
