Improving the Algorithm: The Inclusion of a Socioeconomic Status Measure in Predicting Type 2 Diabetes Development in Youth with Prediabetes

Objective. To evaluate whether the Area Deprivation Index (ADI) contributes to predicting type 2 diabetes development in youth with prediabetes, compared to a machine-learning (ML) model built with other data elements. Research design and methods. Patient encounters (N=665) from an Electronic Medical Record were utilized to build supervised ML models to predict type 2 diabetes development within one year of prediabetes diagnosis. ADI was constructed using patients’ census block data. Two models, trained on data with and without ADI, were built. The model selection resulted in logistic regressions with (1) HbA1c-only and (2) HbA1c+ADI as the best models from each dataset. Results. A total of 181(27.2%) encounters developed type 2 diabetes. The area under the receiver operator curve of the HbA1c-only model was 0.68, and the HbA1c+ADI model was 0.73. Conclusion. The addition of ADI to the model resulted in the best performance in predicting youth-onset type 2 diabetes development.