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FP-Ail, Frequent Pattern, Health Database, Knowledge

Health databases are characterised by large number of attributes such as personal biological and diagnosis information, health history, prescription, billing information and so on. The increasing need for providing enhanced medical system has necessitated the need for adopting an efficient data mining technique for extracting hidden and useful information from health database. In the past, many data mining algorithms such as Apriori, Eclat, H-Mine have been developed with deficiency in time-space trade off. In this work, an enhanced FP-growth frequent pattern mining algorithm coined FP-Ail is applied to students’ health database with a view to provide information about prevalent ailments and suggestions for managing the identified ailments. FP-Ail is tested on a student’s health database of a tertiary institution in Nigeria and the results obtained could be used by the management of the health centre for enhanced strategic decision making about health care. FP-Ail also provides the possibility to refine the minimum support threshold interactively, and to see the changes instantly.