Type 2 diabetes is most common in older people, but it is becoming more frequent among young adults, who face a higher risk of early and severe complications. Missing the diagnosis in this group can have serious consequences for their health. This study found that common machine learning models show what the authors call “digital ageism”: they miss diabetes more often in young adults aged 30 to 44 than in other age groups.
The researchers used data from a large US health survey with more than 400,000 participants, in which young adults are a minority of the people with diabetes. Instead of using a single model for everyone, they trained a separate model for each five-year age band (30–34, 35–39 and 40–44), adjusting the data balance within each group.
This approach improved the detection of diabetes in young adults by 26% to 40%, and performed better than seven commonly used techniques for balancing data. The authors note that a model trained for one age group did not work equally well for groups defined by gender or ethnicity, but that the same approach could be applied to other underrepresented groups.
Learn more about this study here: https://doi.org/10.1109/JBHI.2025.3616312
Reference:
Pias, T. S., Su, Y., Tang, X., Wang, H., Faghani, S., & Yao, D. (2026). Enhancing fairness and accuracy in diagnosing type 2 diabetes in young adult population. IEEE Journal of Biomedical and Health Informatics, 30(4), 3321–3330.