Analysis of Bacterial Association Patterns that trigger Bacterial Vaginosis

  • Freddy De la Cruz-Ruiz División Académica de Ciencias de la Tecnología y la Información
  • Juana Canul-Reich División Académica de Ciencias y Tcnologías de la Información. Universidad Juárez Autónoma de Tabasco, Km. 1 Carretera Cunduacán-Jalpa de Méndez, Col. La Esmeralda, Cunduacán, 86690, Tabasco, México.
  • Erick de la Cruz-Hernández División Académica Multidisciplinaria de Comalcalco. Universidad Juárez Autónoma de Tabasco, Ranchería Sur 4ta. sección. Comalcalco, Tabasco, México.
  • Rafael Rivera-Lopez Departamento de Sistemas y Computación. Tecnológico Nacional de México. Instituto Tecnológico de Veracruz, Av. Miguel Angel de Quevedo 2779, Formando Hogar, 91897 Veracruz, Veracruz, México.
Keywords: Bacterial Vaginosis, Machine learning, Association rules, Quality metrics, ARules package functions.

Abstract

Background: Bacterial Vaginosis (BV) is a dysbiosis of the normal flora residing in the patient’s vaginal mucosa. Objective: Running the apriori algorithm to mine association rules in a dataset that holds records of patients diagnosed with BV+. Method: To select the rules created with statistical significance the functions is.redundant, is.significant, and is.maximal were used. Also eight quality metrics were used. Results: The best percentage of support to find the frequent itemsets was 7%. The confidence percentage to create the rules was 90%. The best metric was Fisher’s exact test. The algorithm reported 58 rules. After selection with the fucntions and metrics, 17 rules were reported. Biological validation reduced the rules to 5. Rules reported that Atopobium vaginae, Gardnerella vaginalis, Megasphaera phylotype 1, and Ureaplasma parvum interact with each other to develop BV+. Conclusion: Knowing the bacteria (patterns) involved in the development of BV supports in the diagnosis of BV+.

 

Published
2022-08-16
How to Cite
De la Cruz-Ruiz, F., Canul-Reich, J., de la Cruz-Hernández, E., & Rivera-Lopez, R. (2022). Analysis of Bacterial Association Patterns that trigger Bacterial Vaginosis. International Journal of Combinatorial Optimization Problems and Informatics, 13(4), 83-102. Retrieved from https://ijcopi.org/ojs/article/view/301
Section
Articles