The current health-care system in Saudi Arabia is not well equipped with proper technology and tools for predicting health risks in the Kingdom’s population. This results in inefficient allocation of resources, in that individuals who are not at risk may receive unnecessary screening and potentially treatments, while higher risk patients may not receive the necessary intervention and treatment due to inadequate resources, or lack of accurate screening and/or timely intervention. In this project we developed data-driven models and machine learning algorithms to address these problems in a novel and cost-effective way.

The main objectives of this project were twofold: The first was to develop a data analytics framework for modeling and assessment of adverse outcome risks due to chronic diseases, with particular focus on cardiovascular diseases in individuals in the Saudi population. The second was a data-driven intervention and control framework for optimal resource allocation based on the assessed risk to provide timely and efficient treatment.