Using Advanced Analytics to Model and Predict Risk Factors of Non-Communicable Diseases

Pulse Lab New York

Project Description

Non-communicable diseases (NCDs) such as cardiovascular diseases, cancer, diabetes and chronic respiratory diseases are responsible for 70% per cent of all deaths globally, according to the World Health Organization (WHO). NCDs disproportionately affect people in low- and middle-income countries where more than three quarters of global NCD deaths – 31 million – occur.

There is an opportunity to use the volume of health, behavioural, socioeconomic and environmental data that are generated today, to understand risk factors and health outcomes associated with NCDs.

Based on this work, Global Pulse and the WHO Global Coordination Mechanism on NCDs are developing an advanced analytics platform. The platform will use machine learning and deep learning methods to model and predict known and new NCD risk factors – such as tobacco use and alcohol consumption – as well as NCD health outcomes and behaviours. The objective is to enable timely and detailed insights into the complexities of the prevalence of NCDs and their impact on policy action.

Paper: Using big data for non-communicable disease surveillance

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