Academic Journal
Key predictive factors of breast cancer based on race using machine learning models.
| Τίτλος: | Key predictive factors of breast cancer based on race using machine learning models. |
|---|---|
| Συγγραφείς: | Yin S; School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA., Nanda G; School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA., Sundararajan R; School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA. Electronic address: raji@purdue.edu. |
| Πηγή: | Annals of epidemiology [Ann Epidemiol] 2026 Jul; Vol. 119, pp. 110080. Date of Electronic Publication: 2026 Mar 28. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: United States NLM ID: 9100013 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2585 (Electronic) Linking ISSN: 10472797 NLM ISO Abbreviation: Ann Epidemiol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York, NY : Elsevier, c1990- |
| Ιατρικοί όροι (MeSH): | Breast Neoplasms*/ethnology , Breast Neoplasms*/epidemiology , Racial Groups*/statistics & numerical data , Boosting Machine Learning Algorithms*, Mammography/statistics & numerical data ; Adult ; Aged ; Female ; Humans ; Middle Aged ; Young Adult ; Age Factors ; Bayes Theorem ; Classification Algorithms ; Incidence ; Logistic Models ; Prediction Algorithms ; Predictive Learning Models ; Risk Factors ; White |
| Περίληψη: | Purpose: In this research, key factors influencing breast cancer risk, a major global issue, are investigated, using machine learning (ML) and explainable AI, for racial differences. Methods: We used Breast Cancer Surveillance Consortium (BCSC) data, originally comprising 1.5 million unique combination records, from 6.7 million mammograms, collected between 2005 and 2017. Naïve Bayes, Logistic Regression, and Extreme Gradient Boosting models were applied to identify these key predictors. Variable importance and SHapley Additive exPlanations values were used to interpret models and identify most predictive factors. Analyses were stratified by six racial groups. Results: History of biopsy (50.04%) and age group (25.85%) were the strongest predictors across all models and races. Menopausal status, breast density, and age at first childbirth were also important. White women had the highest overall incidences, particularly those over 65 (9.02 overall; 18.13 at age 65 + per 100,000), while Black women had higher rates in younger age groups (7.1 per 100,000 at age 18-29). Native American women showed higher rates in certain older age groups, whereas Asian/Pacific Islander and Other/Mixed groups had generally lower rates. Conclusions: ML and explainable AI applied to BCSC data identified key predictors and highlighted racial disparities among most predictive factors for breast cancer risk. (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: BCSC Dataset; Breast cancer; Machine leaning models; Racial disparity; SHAP; XGBoost |
| Entry Date(s): | Date Created: 20260330 Date Completed: 20260714 Latest Revision: 20260714 |
| Update Code: | 20260715 |
| DOI: | 10.1016/j.annepidem.2026.110080 |
| PMID: | 41912057 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1873-2585 |
|---|---|
| DOI: | 10.1016/j.annepidem.2026.110080 |