Machine Learning Based Metagenomic Prediction of Inflammatory Bowel Disease.
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.
['Mihajlović|Andrea|A|', 'Mladenović|Katarina|K|', 'Lončar-Turukalo|Tatjana|T|', 'Brdar|Sanja|S|']
D039903:Actinobacteria D041963:Bacteroidetes D003093:Colitis, Ulcerative D003424:Crohn Disease D000069196:Gastrointestinal Microbiome D006801:Humans D015212:Inflammatory Bowel Diseases D000069550:Machine Learning D045202:Porphyromonas endodontalis D041181:Staphylococcus hominis
machine learning imbalance microbiome feature selection