A Data Mining Algorithm for Association Rules with Chronic Disease Constraints

Abstract

The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China’s medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved.

Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model

“…Vougas et al [6] described a novel computer screening process based on association rule mining, used to identify genes as candidate driving factors for drug response. Liu et al [7] proposed an association rule data mining algorithm that combines clustering matrices and pruning strategies, reducing the number of database scans and generating an appropriate number of candidate itemsets, significantly reducing runtime. Hadavi et al [8] utilized medical records spanning 5 years from 512 esophageal cancer patients and those with related issues to create six significant association rules.…”