Affiliations: [a] Academic Division of Sciences and Information Technologies, Juarez Autonomous University of Tabasco, Cunduacán-Jalpa KM highway. 1 Col. The Esmeralda, Cunduacán, Tabasco, Mexico | [b] Comalcalco Multidisciplinary Academic Division, Juarez Autonomous University of Tabasco, Ranchería South 4th. Section. Comalcalco, Tabasco, Mexico
Correspondence:
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Corresponding author: Juana Canul-Reich, Academic Division of Information Sciences and Technologies, Juarez Autonomous University of Tabasco, Cunduacan-Jalpa KM highway. 1 Col. The Esmeralda, Cunduacan, Tabasco, Mexico. Tel./Fax: +52 3581500 ext 6727; E-mail: juana.canul@ujat.mx.
Abstract: Polymicrobial syndromes such as Bacterial Vaginosis (BV), where there is a great diversity of microorganisms and causal connotations, turn it into a disease with complex dynamics in the bacteria’s coexistence in groups of patients. The main aim of this study was to explore a dataset of patients with BV to determine a more informed number of groups to create for further analysis of bacteria’s coexistence. The Agglomerative Hierarchical Clustering (AHC) algorithm was applied to a BV dataset from an urban population in southeastern Mexico consisting of 201 patient records with 59 patient attributes and three classes (BV-positive, BV-negative, BV-indeterminate). In the clustering results obtained, it is possible to identify different remarkable groups of patients. The most prevalent coexisting bacteria among patients with BV were Atopobium + Gardnerella vaginalis with 37.50%, Atopobium + Megasphaera with 15.68% in the first experiment. Whereas, in the second experiment, the coexisting bacteria were Atopobium + Megasphaera + Mycoplasma hominis with 33.33% and Atopobium + Gardnerella vaginalis + Mycoplasma hominis with 25%. Finally, we provided evidence that via the AHC algorithm, it was possible to identify an optimal number of clusters with high intra-similarity and inter-dissimilarity. Furthermore, this approach allowed us to create a clustering model that helps analyze the complex dynamics between bacteria in groups of patients with BV.
Keywords: Hierarchical clustering, bacterial vaginosis, data mining, coexisting bacteria
