An agglomerative hierarchical clustering approach to identify coexisting bacteria in groups of bacterial vaginosis patients

Autor: Henry Jesús Hernández-Gómez, Juana Canul-Reich, Betania Hernández-Ocaña, Erick de la Cruz Hernández
Rok vydání: 2023
Předmět:
Zdroj: Intelligent Data Analysis. 27:583-611
ISSN: 1571-4128
1088-467X
Popis: 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.
Databáze: OpenAIRE
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