SmartCHANGE : AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth

Non-communicable diseases (NCDs) are the leading cause of death and healthcare expense. Common risk factors for many of them are
obesity and low physical fitness resulting from an unhealthy lifestyle. Targeting children and youth for lifestyle interventions has been
suggested because (1) early precursors of most NCDs are already present at this age, (2) childhood and adolescence are critical periods
for the acquisition of healthy lifestyle habits, and (3) unhealthy lifestyle in this age group is prevalent. We propose to develop long-term
risk-prediction models for cardiovascular and metabolic disease for people aged 5–19. We have already identified 15 datasets with data
on behaviour, fitness, biomarkers and actual NCDs spanning various ages. We will develop machine-learning methods that can train
models on such heterogeneous datasets, enabling the prediction of risk for people of various ages for whom different data is available.
We will employ federated learning for data privacy, carefully curate and balance the data to ensure it is bias-free and representative of the
target group, and employ methods for explanation and visualisation of the data, models and predictions.