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Exploring Risk Factors of Adverse Maternal and Child Health Outcomes Using Machine Learning and Other Advanced Data Analytical Approaches

Combining multiple data sets from HBGDKi using ML tools for prediction, classification and topic discovery may yield new insights for adverse birth outcomes and intermediate outcomes of interest. The study is based on a set of epidemiological, clinical and biochemical variables risk stratification algorithms for various adverse outcomes with practical applicability in health programme, and clinical settings may be feasible to develop using ML tools.ML can be used to suitably impute/bin missing values within datasets and merge variables from multiple datasets using robust data triangulation algorithms.

More information about Data Science Approaches to Improve Maternal and Child Health in India

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The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.