Background
Continuum of care for RMNCH refers to integrated care for the health of mothers, neonates and children. The benefit of utilizing Machine Learning (ML) to predict under-five mortality outcome have not been evaluated in any previous study. Therefore, the aim of this study is to use ML to evaluate the association between continuum of RMNCH care and all-cause under five mortality.
Method
In this study, we utilised data from the Multiple Indicator Cluster Survey (MICS), and the Demographic and Health Survey (DHS). Analysis was conducted on data from five Low and Middle-Income Countries (LMICs), across five UNICEF sub-regions. ML approach was used to explore the association between under-five mortality and integrated RMNCH care, which comprises of: Preconception care, Antenatal care, Skilled Birth services, Postnatal care, childhood and neonatal health and nutritional services.
Findings
We will report the Receiver-Operating Characteristics (ROC) for prediction of all-cause under-five mortality. The Area-Under-the-Curve findings on continuum of care and under-five mortality prediction will be enumerated.
Conclusion
Based on our findings, we will draw inference on how the continuum of care for RMNCH predicts under-five mortality. Our findings will potentially provide a decision-tool that can guide maternal and child health service delivery and help identify interventions that needs to be prioritized.