Project description
AI-based health risk prediction models for the young
Obesity and a lack of physical fitness are risk factors for various non-communicable diseases (NCDs). Many NCDs have early precursors that manifest at a young age, and unhealthy lifestyles are prevalent during this period. The EU-funded SmartCHANGE project aims to develop long-term risk prediction models for cardiovascular and metabolic diseases in individuals aged 5 to 19. The project will use 15 data sets containing information on behaviour, fitness levels, biomarkers, and actual NCD cases across different age groups. Machine-learning methods will enable accurate risk prediction, while federated learning techniques will ensure data privacy. These methods will be leveraged to design two applications (one for health professionals and another for citizens). The project's solution will be tested in relevant healthcare settings.
Objective
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. Participatory design involving explanation of the AI will be used to design two applications: one for health professionals and the other for citizens. Both will show the risks broken down by risk factors, and the recommended behaviour changes to reduce them, in a manner appropriate for each user group. The developed solution will be validated in a large proof-of-concept study in four countries involving different health settings (family, school, primary care, integrated care …).
To facilitate practical use of the developed solution, we will prepare recommendations for their implementation, and a realistic exploitation plan. These activities will be supported by dissemination and communication activities specifically tailored to the target groups (e.g. involving science museums).
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Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
1000 Ljubljana
Slovenia