Finding Association Patterns of Disease Co-occurrence by using Closed Association Rule Generation

Abstract

This paper proposes a closed association rule generation technique to investigate the association patterns of diseases that are frequent co-occurrence. Diseases records of 5,000 patients are studied to find the association patterns of disease co-occurrence. The CHARM algorithm is adapted to find frequent diseases that can cover all-important patterns with a small number. Then the association patterns of disease co-occurrence are created in a form of association rules from the frequent diseases. The rules represent diseases associated with other diseases. Accuracy and prediction ratio are defined to evaluate the generated association patterns. From the experimental results, the generated association patterns give 79.76% of accuracy and 84.03% of prediction ratio although the number of generated association patterns is small. Moreover, the top-10 association patterns of disease cooccurrence are investigated. Besides, the 5 most frequent diseases are found to deeply study the other related diseases of them. From the investigation, we found that diabetes mellitus, metabolic disorders, and renal failure are highly related to hypertensive diseases with 88.81% of confidence. In addition, we found that influenza and pneumonia, plastic and other anemias are highly related to metabolic disorders.