Mining association rules from clinical databases: an intelligent diagnostic process in healthcare

Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures, from large amounts of data stored in databases, data warehouses, or other information repositories. Mining Associations is one of the techniques involved in the process mentioned above and used in this paper. Association is the discovery of association relationships or correlations among a set of items. The algorithm that was implemented is a basic algorithm for mining association rules, known as a priori. In Healthcare, association rules are considered to be quite useful as they offer the possibility to conduct intelligent diagnosis and extract invaluable information and build important knowledge bases quickly and automatically. The problem of identifying new, unexpected and interesting patterns in medical databases in general, and diabetic data repositories in specific, is considered in this paper. We have applied the a priori algorithm to a database containing records of diabetic patients and attempted to extract association rules from the stored real parameters. The results indicate that the methodology followed may be of good value to the diagnostic procedure, especially when large data volumes are involved. The followed process and the implemented system offer an efficient and effective tool in the management of diabetes. Their clinical relevance and utility await the results of prospective clinical studies currently under investigation.