An improved itemset generation approach for mining medical databases

Finding frequent patterns in data mining plays a significant role for finding the relational patterns. In this study an extendable and improved itemset generation approach has been constructed and developed for mining the relationships of the symptoms and disorders in the medical databases. The algorithm of the developed software finds the frequent illnesses and generates association rules using Apriori algorithm. The developed software can be usable for large medical and health databases for constructing association rules for disorders frequently seen in the patient and determining the correlation of the health disorders and symptoms observed simultaneously.