Mining Association Rules from Clinical Databases: An Intelligent Diagnostic Process in Healthcare

TLDR

Data mining discovers patterns and associations in large datasets, and in healthcare, association rules enable intelligent diagnosis and rapid knowledge base construction. The study aims to identify novel, unexpected patterns in medical databases, specifically diabetic patient records. Using the apriori algorithm, the authors extracted association rules from a diabetic patient database containing real clinical parameters. The extracted rules proved useful for diabetes diagnosis and management, especially with large data volumes, though clinical relevance awaits prospective validation.

Abstract

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 apriori 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.