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Evaluation of race/ethnicity-specific survival machine learning models for Hispanic and Black patients with breast cancer

 Jung In Park  ;  Selen Bozkurt  ;  Jong Won Park  ;  Sunmin Lee 
 BMJ HEALTH & CARE INFORMATICS, Vol.30(1) : e100666, 2023-01 
Journal Title
Issue Date
Black People ; Breast Neoplasms* ; Ethnicity* ; Female ; Hispanic or Latino ; Humans ; Proportional Hazards Models
artificial intelligence ; health equity ; informatics ; machine learning
ObjectivesSurvival machine learning (ML) has been suggested as a useful approach for forecasting future events, but a growing concern exists that ML models have the potential to cause racial disparities through the data used to train them. This study aims to develop race/ethnicity-specific survival ML models for Hispanic and black women diagnosed with breast cancer to examine whether race/ethnicity-specific ML models outperform the general models trained with all races/ethnicity data.MethodsWe used the data from the US National Cancer Institute's Surveillance, Epidemiology and End Results programme registries. We developed the Hispanic-specific and black-specific models and compared them with the general model using the Cox proportional-hazards model, Gradient Boost Tree, survival tree and survival support vector machine.ResultsA total of 322 348 female patients who had breast cancer diagnoses between 1 January 2000 and 31 December 2017 were identified. The race/ethnicity-specific models for Hispanic and black women consistently outperformed the general model when predicting the outcomes of specific race/ethnicity.DiscussionAccurately predicting the survival outcome of a patient is critical in determining treatment options and providing appropriate cancer care. The high-performing models developed in this study can contribute to providing individualised oncology care and improving the survival outcome of black and Hispanic women.ConclusionPredicting the individualised survival outcome of breast cancer can provide the evidence necessary for determining treatment options and high-quality, patient-centred cancer care delivery for under-represented populations. Also, the race/ethnicity-specific ML models can mitigate representation bias and contribute to addressing health disparities.
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1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
Yonsei Authors
Park, Jong Won(박종원)
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