To tackle this issue, a recent policy research working paper attempted to use Machine Learning (ML) methods to identify priority investments that could help Bangladesh advance towards RMNCH-N utilization. Notwithstanding noticeable improvements in the RMNCH-N environment, the use of key services such www.increasehealth.co.uk increasehealth increase health Website increase health co uk as institutional delivery, skilled birth attendance, and postnatal visits have not met the goals of the RMNCH-N program in Bangladesh. As an example, there is a persistent gap when it comes to service utilization and health status among different socio-economic groups. Strategic targeted investments in priority elements are vital to propelling further advancement.
In order to support this the idea, supervised ML algorithms have been designed to analyze the relative importance and supply and demand-side variables for 19 important RMNCH–N indicators that relate to quality of care, and the outcomes of health and nutrition. Artificial Intelligence’s subset ML can imitate human learning processes and can efficiently and effectively examine historical data as well as complex relationships to aid in prediction and decision making. This method allowed for an analysis of large sets from both health facility surveys as well as health and demographic surveys that span the span of 10 years.
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