  Diabetes affects over 400 million people and is among the leading causes of
morbidity worldwide. Identification of high-risk individuals can support early
diagnosis and prevention of disease development through lifestyle changes.
However, the majority of existing risk scores require information about
blood-based factors which are not obtainable outside of the clinic. Here, we
aimed to develop an accessible solution that could be deployed digitally and at
scale. We developed a predictive 10-year type 2 diabetes risk score using 301
features derived from 472,830 participants in the UK Biobank dataset while
excluding any features which are not easily obtainable by a smartphone. Using a
data-driven feature selection process, 19 features were included in the final
reduced model. A Cox proportional hazards model slightly overperformed a
DeepSurv model trained using the same features, achieving a concordance index
of 0.818 (95% CI: 0.812-0.823), compared to 0.811 (95% CI: 0.806-0.815). The
final model showed good calibration. This tool can be used for clinical
screening of individuals at risk of developing type 2 diabetes and to foster
patient empowerment by broadening their knowledge of the factors affecting
their personal risk.
