Abstract
Bacterial vaginosis develops as a consequence of the displacement Lactobacillus producing lactic acid and hydrogen peroxide which protect the vaginal mucosa. A dataset consisting of 201 observations and 58 variables was tested. Apriori, Eclat, and FP-Growth algorithms created the association rules. Rules with statistical significance were selected with the quality metrics and the functions. Validation was performed to select biologically significant rules supported by the AWK programming language. The bacterial patterns presented by each algorithm when the dataset was balanced were of higher quality with respect to the patterns presented by each algorithm when the dataset was unbalanced. The algorithm with the lowest performance was Apriori. The Eclat algorithm performed well with respect to the number of rules reported. The FP-Growth algorithm performed well with respect to the quality of the reported pattern. Knowing the bacteria that these algorithms locate in the antecedent of the association rule is extremely important since this guides the physician objectively to attack the problem that bacterial vaginosis represents.
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Ruiz, F.d.l.C., Reich, J.C. (2023). Apriori, Eclat, and FP-Growth Algorithms to Study Bacterial Vaginosis. In: Kumar, S., Hiranwal, S., Purohit, S., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies. ICCCT 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-3485-0_79
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