Diabetes is considered as one of the deadliest diseases in the world, therefore medical professionals need a reliable prediction and decision making methodology. The main aim of this paper is to implement data mining in diabetes diagnosis to discover new patterns and to interpret the data patterns to provide meaningful and useful information for medical practitioners. The analytical technique for this project consists of five stages data collection, preprocessing, feature extraction, implementation and then interpretation and evaluation. In this research, the analytical technique implores the use of k-means clustering algorithm and association rules (A-priori algorithm) was used to analyse diabetes dataset collected from two hospitals in Ondo State, Nigeria. The analytical technique proposed was implemented in PyCharm Community Edition. Three clusters were generated using K-means clustering algorithm and A-priori algorithm was used to generate patient’s profile for each cluster. Performance evaluation on the technique was carried out using accuracy and showed result of 85% which indicates that the technique is efficient.

Data Mining, Diabetes, K-Means Clustering, Association Rule Mining

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