Assessment of Association Rule Mining Using Interest Measures on the Gene Data

Aim: Data mining is the discovery process of beneficial information, not revealed from large-scale data beforehand. One fields in which widely used health. With mining, diagnosis and treatment disease risk factors affecting can be determined quickly. Association rules are one techniques. The aim this study to determine patient profiles by obtaining strong association with apriori algorithm, rule algorithms.
 Material Method: set consists 205 acute myocardial infarction (AMI) patients. patients have also carried genotype FNDC5 (rs3480, rs726344, rs16835198) polymorphisms. Support confidence measures evaluate obtained Apriori algorithm. these correct but strong. Therefore, interest used, besides two basic measures, stronger rules. In For reaching rules, lift, conviction, certainty factor, cosine, phi mutual information applied.
 Results: study, 108 were obtained. proposed implemented reach as a result 29 qualified strong.
 Conclusion: As result, been use clinical decision making process. Thanks obtained, it will facilitate profile determination decision-making AMI