Finding risk factors in pregnancy related to neonatal hypoxia is a challenging task due to the informal nature and a wide scatter of the data. In this work, we propose a methodology for sequential estimation of interestingness of association rules with two sets of criteria. The rules suggest that a strong relationship exists between the specific sets of attributes and the diagnosis. We set up a profile of the pregnant woman with a high likelihood of hypoxia of the newborn that would be beneficial to medical professionals.
CITATION STYLE
Skarga-Bandurova, I., Biloborodova, T., & Nesterov, M. (2017). Discovering interesting associations in gestation course data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10423 LNAI, pp. 204–214). Springer Verlag. https://doi.org/10.1007/978-3-319-65340-2_17
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