Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.

ABSTRACT: Type 2 diabetes has become an urban epidemic influenced by neighbourhood environments. However, conventional risk models focusing solely on individual factors fail to account for these neighbourhood influences and often require detailed patient data that may not be available. To address this gap, we developed an integrated approach combining machine learning and causal inference to map type 2 diabetes risk at the neighbourhood level. Using demographic, health, and socioeconomic data from 1,149 Census Tracts (CTs; the neighbourhood unit in this study) in a large metropolitan region, we trained seven machine learning models to identify neighbourhoods with high diabetes prevalence. Although neighbourhood-level diabetes data were available for this study area, our model's high predictive accur

SUBMITTER: Noaeen M 

PROVIDER: S-EPMC12858814 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature