Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1355Keywords:
Blood Cell Count, Machine Learning, Breast CancerAbstract
Objective: To evaluate the efficacy of machine learning (ML) in using complete blood count (CBC) for breast cancer risk assessment. Method: This retrospective study analyzed CBCs from 396,848 women aged 40 to 70. A total of 2861 cases were identified (1882 confirmed by biopsy and 979 by imaging), while 393,987 were controls (BI-RADS 1 or 2). Data were divided into modeling (training and validation) and testing sets based on diagnostic certainty. Results: The ridge regression model, incorporating the neutrophil-to-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64. The study population was stratified into four risk groups: high, moderate, medium, and low, with relative ratios of 1.99, 1.32, 1.02, and 0.42, respectively. Conclusion: This ML model provides a cost-effective tool for personalized breast cancer screening, potentially improving early detection in resource-limited settings.
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