Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data
This study concluded that, in BC risk prediction, incorporating specific risk factors, such as genetic and image-related variables, may be necessary to further enhance accuracy in BC risk prediction models. Furthermore, it is crucial to address modeling issues in models with a restricted number of features for future research.
Authors
- Mohammad Barati (ORCID: https://orcid.org/0000-0001-7717-5181)
- Ali Sarmadi
- Behzad Amanpour‐Gharaei (ORCID: https://orcid.org/0009-0003-7250-2106)
- Seyed‐Ali Sadegh‐Zadeh (ORCID: https://orcid.org/0000-0001-6197-3410)
- Saeid Amanpour (ORCID: https://orcid.org/0000-0002-0395-418X)
- Zohreh Hasani
- Mozafar Saadat (ORCID: https://orcid.org/0000-0002-6622-4715)
- Kazem Zendehdel (ORCID: https://orcid.org/0000-0002-0269-4945)
- Haniyeh Rafiepoor
- Sepideh Hajivalizadeh (ORCID: https://orcid.org/0000-0003-0393-1196)
- Alireza Ghorbankhanloo
- Zahra Zangeneh Madar
Institutions
- University of Staffordshire (GB)
- University of Tehran (IR)
- Iran University of Science and Technology (IR)
- Tehran University of Medical Sciences (IR)
- University of Birmingham (GB)
- K. N. Toosi University of Technology (IR)
Publication Details
- Journal
- Cancer Reports
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1002/cnr2.70175
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Tehran University of Medical Sciences and Health Services