Public Health Management for Maternal Mortality Reduction in Smart Cities

More than half a million women die each year from pregnancy-related causes, with 99% of these deaths occurring in developing countries. The rapid development of smart cities in some of these developing countries, coupled with technological advances, offers an opportunity for better management of maternal healthcare. Using Sindh, Pakistan as a case study, we present an automated system for early prediction of maternal mortality rate (MMR). Previous prediction models were not effective due to a lack of integration of healthcare program datasets to identify the variables influencing MMR across healthcare programs. The novel system was validated through an integrated approach, applying different machine learning algorithms to MMR datasets taken from Sindh healthcare programs. With prediction accuracies >90%, public health officials now have an easy-to-use tool to gain insight into factors that influence maternal maternity, which can lead to MMR reduction. Throughout the study, we have ensured that all research steps and findings can be broadly applicable to other smart city environments.