Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has ledto the development of a computational model that recapitulates the aetiology of thedisease and simulates the immunological and metabolic alterations linked to type-2diabetes subjected to clinical, physiological, and behavioural features of prototypicalhuman individuals.Results: We analysed the time course of 46,170 virtual subjects, experiencing differentlifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes.Conclusions: The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of thedetailed mathematical model, ready to be implemented on mobile devices to allowself-assessment by informed and aware individuals. The computational model used togenerate the dataset of this work is available as a web-service at the following address:http://kraken.iac.rm.cnr.it/T2DM