Exploration mining in diabetic patients databases: Findings and conclusions

Abstract

Real-life data mining applications are interesting because they often present a different set of problems for data miners. One such real-life application that we have done is on the diabetic patients databases. Valuable lessons are learnt from this application. In particular, we discover that the often neglected pre-processing and post-processing steps in knowledge discovery are the most critical elements in determining the success of a real-life data mining application. In this paper, we shall discuss how we carry out knowledge discovery on this diabetic patient database, the interesting issues that have surfaced, as well as the lessons we have learnt from this application. We will describe a semi-automatic means for cleaning the diabetic patient database, and present a step-by-step approach to help the health doctors explore their data and to understand the discovered rules better.