Fig. 1: Faecal microbiome-based machine learning for multi-class disease diagnosis.

a Framework for dataset partition, model training and independent validation. b Area under the receiver operating characteristic curve (AUROC, centre for the error bands is median). c Performance metric details of the trained random forest multi-class classifier for classifying one phenotype from all others using species-level faecal microbiome data in the independent test set. SVM support vector machine, KNN K-nearest neighbours, RF random forests; MLP multi-layer perceptron, GCN graph convolutional neural network, CA colorectal adenomas, CD Crohn’s disease, CRC colorectal cancer, CVD cardiovascular disease, IBS-D diarrhoea-dominant irritable bowel syndrome, PACS post-acute COVID-19 syndrome, UC ulcerative colitis. Source data are provided as a Source Data file.