Development and Validation of a Machine Learning–Based Screening Algorithm to Predict High-Risk Hepatitis C Infection

Background Amid the opioid epidemic in the United States, Hepatitis C virus (HCV) infections are rising, with one-third of individuals with infection unaware due to the asymptomatic nature. This study aimed to develop and validate a machine learning (ML)-based algorithm to screen individuals at high risk of HCV infection. Methods We conducted prognostic modeling using the 2016-2023 OneFlorida+ database of all-payer electronic health records. The study included individuals aged ≥18 years who were tested for HCV antibodies, RNA, or genotype. We identified 275 features of HCV, including sociodemographic and clinical characteristics, during a 6-month period before the test result date. Four ML algorithms—elastic net (EN), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN)—were developed and validated to predict HCV infection. We stratified patients into deciles based on predicted risk. Results Among 445,624 individuals, 11,823 (2.65%) tested positive for HCV. Training (75%) and validation (25%) samples had similar characteristics (mean [SD] age, 45 [16] years; 62.86% female; 54.43% White). The GBM model (C statistic, 0.916 [95% CI, 0.911-0.921]) outperformed the EN (0.885 [0.879-0.891]), RF (0.854 [0.847-0.861]), and DNN (0.908 [0.903-0.913]) models (P<0.0001). Using the Youden index, GBM achieved 79.39% sensitivity and 89.08% specificity, identifying one positive HCV case per six tests. Among patients with HCV, 75.63% and 90.25% were captured in the top first and first to third risk deciles, respectively. Conclusions ML algorithms effectively predicted and stratified HCV infection risk, offering a promising targeted screening tool for clinical settings.