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

摘要

Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients. In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statistical model able to recapitulate the simulated dynamics. The resulting machine learning model adequately predicts the synthetic data 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.