Development of a Machine Learning Modeling Tool for Predicting Human Immunodeficiency Virus Incidence Using Public Health Data From a County in the Southern United States

Background Advancements in machine learning (ML) have improved the accuracy of models that predict human immunodeficiency virus (HIV) incidence. These models have used electronic medical records and registries. We aim to broaden the application of these tools by using deidentified public health datasets for notifiable sexually transmitted infections (STIs) from a southern US county known for high HIV incidence. The goal is to assess the feasibility and accuracy of ML in predicting HIV incidence, which could inform and enhance public health interventions.Methods We analyzed 2 deidentified public health datasets from January 2010 to December 2021, focusing on notifiable STIs. Our process involved data processing and feature extraction, including sociodemographic factors, STI cases, and social vulnerability index (SVI) metrics. Various ML models were trained and evaluated for predicting HIV incidence using metrics such as accuracy, precision, recall, and F1 score.Results We included 85 224 individuals; 2027 (2.37%) were newly diagnosed with HIV during the study period. The ML models demonstrated high performance in predicting HIV incidence among males and females. Influential features for males included age at STI diagnosis, previous STI information, provider type, and SVI. For females, predictive features included age, ethnicity, previous STI information, overall SVI, and race.Conclusions The high accuracy of our ML models in predicting HIV incidence highlights the potential of using public health datasets for public health interventions such as tailored HIV testing and prevention. While these findings are promising, further research is needed to translate these models into practical public health applications. We applied machine learning to predict HIV incidence in a high-incidence US county using deidentified public health data, focusing on sociodemographic factors and sexually transmitted infections. The models showed high accuracy, suggesting the potential for guiding public health strategies.