TITLE:
Maternal and Child Health Care Quality Assessment: An Improved Approach Using K-Means Clustering
AUTHORS:
Sarah Nyanjara, Dina Machuve, Pirkko Nykanen
KEYWORDS:
Maternal Health Quality, Clustering Model, Health Quality Assessment, Maternal Health Assessment
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.10 No.3,
August
8,
2022
ABSTRACT: High maternal and child deaths in developing countries are frequently
linked to poor health services provided to pregnant women and children. To
improve the quality of maternal, neonatal and child health (MNCH) services, the
government and other stakeholders in MNCH emphasize the importance of quality
assessment. However, effective quality assessment approaches are mostly lacking
in most developing countries, particularly in Tanzania. This study, therefore,
aimed at developing a quality assessment approach that can effectively assess
and report on the quality of MNCH services. Due to the need for a good quality
assessment approach that suits a resource-constrained environment, machine learning-based approach was proposed and developed. K-means algorithm was used to develop a
clustering model that groups MNCH data and performs cluster summarization to
discover the knowledge portrayed in each group on the quality of MNCH services.
Results confirmed the clustering model’s ability to assign the data points into
appropriate clusters; cluster analysis with the collaboration of MNCH experts
successfully discovered insights on the quality of services portrayed by each
group.