Estimating Countries with Similar Maternal Mortality Rate using Cluster Analysis and Pairing Countries with Identical MMR
This work addresses maternal health disparities by grouping countries for targeted interventions, but it is incremental as it applies standard clustering methods to existing data without novel methodological contributions.
The research tackled the problem of identifying countries with similar and opposite maternal mortality rates (MMR) by applying unsupervised machine learning for cluster analysis on historical data, resulting in the identification of country pairs based on MMR similarities and extremes.
In the evolving world, we require more additionally the young era to flourish and evolve into developed land. Most of the population all around the world are unaware of the complications involved in the routine they follow while they are pregnant and how hospital facilities affect maternal health. Maternal Mortality is the death of a pregnant woman due to intricacies correlated to pregnancy, underlying circumstances exacerbated by the pregnancy or management of these situations. It is crucial to consider the Maternal Mortality Rate (MMR) in diverse locations and determine which human routines and hospital facilities diminish the Maternal Mortality Rate (MMR). This research aims to examine and discover the countries which are keeping more lavish threats of MMR and countries alike in MMR encountered. Data is examined and collected for various countries, data consists of the earlier years' observation. From the perspective of Machine Learning, Unsupervised Machine Learning is implemented to perform Cluster Analysis. Therefore the pairs of countries with similar MMR as well as the extreme opposite pair concerning the MMR are found.