The World Health Organisation indicates that over 70% of deaths worldwide is attributable to chronic non-communicable diseases (NCDs), of which biological risk factors usually emerge in adulthood. Currently, risk prediction tools are used in adults; although, many NCDs have early precursors that manifest at a young age. The identification of high-risk individuals and early behaviour change strategies could improve the primary prevention of NCDs.

SmartCHANGE uses machine-learning methods, that can train models on heterogeneous datasets (i.e. behaviour, fitness levels, biomarkers), to enable accurate risk prediction for people of various ages; while federated learning techniques are employed to ensure data privacy. These methods are leveraged to design two apps: one for health professionals and the other for citizens, which are able to communicate in a bi-directional manner and reduce long-term risk of NCDs. Engineering is responsible for the development of a web-app for health professionals, which integrates an explainable Clinical Decision Support System and for the overall architecture design and subsequent configuration, based on microservices paradigm.

The project has received co-funding from the European Union's Horizon Europe programme - Contract No. 101080965.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. The European Union cannot be held responsible for them.