An explainable, bias-audited machine learning model to detect hidden hepatitis C from region-wide real-world health records

Background: Although direct-acting antivirals have transformed hepatitis C virus (HCV) treatment, 25-30% of active infections in Spain remain undiagnosed. We aimed to develop and validate an interpretable machine-learning model to identify undiagnosed HCV infection using only data already recorded within the health system. Materials and Methods: We conducted a retrospective, population-based cohort study using the Andalusian Population Health Database (BPS). Individuals diagnosed with HCV between 2017 and 2024 (n=25,028) were matched, up to 10:1, to controls by birth year, sex, and health dist

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