Abstract
Association rule mining has been applied actively in order to discover the hidden factors in acute myocardial infarction. There has been minimal research regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarction with ST-segment elevation. In our experiments, we obtain high interestingness factor based on Korean acute myocardial infarction registry which is corrected by 51 participating hospitals since 2005. The interestingness of the factor is evaluated by confidence. It is expected to contribute to prognosis management by high reliability factor.
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Sun Ryu, K., Woo Park, H., Ho Park, S., Ishag, I. M., Hwang Bae, J., & Ho Ryu, K. (2015). The discovery of prognosis factors using association rule mining in acute myocardial infarction with ST-segment elevation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9267, pp. 49–55). Springer Verlag. https://doi.org/10.1007/978-3-319-22741-2_5
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