Annals of epidemiology · 2026
Key predictive factors of breast cancer based on race using machine learning models.
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Yin Shuning, Nanda Gaurav, Sundararajan Raji
Аннотация
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.
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.