Clustering and visualizing the status of child health in Kenya: a data mining approach
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Date
2015-12Author
Njiru, Nicholas M
Type
ThesisLanguage
enMetadata
Show full item recordAbstract
The inauguration of the new constitution in Kenya led to the devolution of health care in the
counties. It is against this backdrop that necessitated a need to develop a model of grouping these
counties into natural groups with similar characteristics that can influence the child health for the
purpose of health care planning and regulation. Little research has explored a methodology that
can be used to create such groupings in Kenya. The purpose of this research was to develop and
explore a methodology of Clustering and Visualizing the status of the child health in Kenya. In
this research we proposed a new model that clustered the counties based on the UNICEF
indicators of child health. The cluster analysis methodology employed to achieve this was by use
of K-Means clustering algorithm. Both hierarchical and non-hierarchical clustering algorithms
were used to build a consensus with the results of clusters obtained by K-Means. The number of
clusters selected was based on heuristic, integrating a statistical-based measure of cluster fit.
Using data from literature, the clustering methodology developed grouped the 47 counties into
three distinctive clusters. These three clusters were made up of 10, 8 and 29 counties
respectively. The study classified the clusters as well-off, most marginalized and moderately
marginalized counties respectively. The methodology developed was objective, replicable and
sustainable to create the clusters. It was developed in a theoretically sound principle and can be
generalized across applications requiring clustering. An examination of several clustering
algorithms revealed similar results.
Publisher
University of Nairobi