Machine Learning Based Metagenomic Prediction of Inflammatory Bowel Disease.
Stud Health Technol Inform
; 285: 165-170, 2021 Oct 27.
Article
em En
| MEDLINE
| ID: mdl-34734869
ABSTRACT
In this study, we investigate faecal microbiota composition, in an attempt to evaluate performance of classification algorithms in identifying Inflammatory Bowel Disease (IBD) and its two types Crohn's disease (CD) and ulcerative colitis (UC). From many investigated algorithms, a random forest (RF) classifier was selected for detailed evaluation in three-class (CD versus UC versus nonIBD) classification task and two binary (nonIBD versus IBD and CD versus UC) classification tasks. We dealt with class imbalance, performed extensive parameter search, dimensionality reduction and two-level classification. In three-class classification, our best model reaches F1 score of 91% in average, which confirms the strong connection of IBD and gastrointestinal microbiome. Among most important features in three-class classification are species Staphylococcus hominis, Porphyromonas endodontalis, Slackia piriformis and genus Bacteroidetes.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Inflamatórias Intestinais
/
Colite Ulcerativa
/
Doença de Crohn
/
Microbioma Gastrointestinal
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Stud Health Technol Inform
Assunto da revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Ano de publicação:
2021
Tipo de documento:
Article