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

• Main Authors: Paola Stolfi, Ilaria Valentini, Maria Concetta Palumbo, Paolo Tieri, Andrea Grignolio, Filippo Castiglione
• Format: Article
• Language: English
• Published: BMC 2020-12-01
• Series: BMC Bioinformatics
• Subjects: Machine learning; Random forest; Emulator; T2D; Computational modeling; Synthetic data
• Online Access: https://doi.org/10.1186/s12859-020-03763-4

Abstract Background 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 led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. Results We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle 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 the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM .