HemoGraph: A Machine Learning Method for Type II Diabetes Pre-screening Using Social and Behavioral Determinants of...

Type II diabetes is a serious problem because of its increasing prevalence in both adults and younger populations worldwide. In this paper, we propose HemoGraph, a deep learning-based method that uses social and behavioral determinants of health for diabetes pre-screening. HemoGraph utilizes graph neural networks to explicitly model the relationships between input features. Adopting the Learning Under Privileged Information (LUPI) framework, HemoGraph learns from lab test results during training, which are not accessible at inference time. We train and evaluate HemoGraph on real-world data from the National Health and Nutrition Examination Survey (1999-2018). Our model reaches a recall of 71.56%, achieving a significant 22% improvement compared to the American Diabetes Association self-test tool, while attaining higher overall performance.