PREVALENCE OF INFECTIOUS DISEASES IN KATSINA STATE: AN INSIGHT USING CLUSTERING APPROACH
Dauda Usman, Ibrahim Lawal Kane · 2012
Data mining is a convenient way of extracting patterns, which represents knowledge implicitly stored in large datasets and focuses on issues relating to their feasibility, usefulness, effectiveness and scalability. This paper presents a hierarchical clustering analysis of infectious disease data of Katsina State. The diseases with a similar degree of prevalence were identified. The result of the cluster formation shows that Malaria is more prevalent in the State, followed by Cholera and Typhoid fever as shown by the Single Linkage and Centroid methods. The Complete Linkage and Ward methods showed that Malaria is the most prevalent followed by Typhoid fever and Cholera in Funtua and Katsina zones, while in Daura zone Typhoid fever is more prevalent followed by Malaria and Cholera.. The Chi-square test for independence indicates that the number of clusters tends to vary from one zone to another. The study concludes that clustering methods is a suitable tool for assessing the level of infections of the disease.