Clustering and Visualizing the Status of Child Health in Kenya: A Data Mining Approach.

International audience The inauguration of the new constitution in Kenya has led to the devolution of health care in the counties. It is against this backdrop that has necessitated the need to develop a model of grouping these regions 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 the 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 propose a new model that clusters 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 12, 8 and 27 observations respectively. The study classified the clusters as well-off, most marginalized and moderately marginalized counties. The methodology developed was objective, replicable and sustainable to create the clusters. It was developed in a theoretically sound principle and can generalize across applications requiring clustering. An examination of several clustering algorithms revealed similar results.