Fig. 1

Workflow of disease classification models construction. We classified eight diseases (IBD: Inflammatory Bowel Disease; CDI: C. difficile Infection; IBS: Irritable Bowel Syndrome; SIBO: Small Intestinal Bacterial Overgrowth; DI: Diabetes; LI: Lactose Intolerance; CD: Cardiovascular Disease; MD: Mental Disorder) with 30 human variables (physiological characteristics, lifestyle, location, and diet) and gut microbial community data (OTUs) obtained from the American Gut Project database using four machine learning techniques (Random Forest, Gradient Boosting Decision Tree, Logistic Regression and eXtreme Gradient Boosting). We propose to build association models by including both human variables and gut microbiota, and assumed that when the performance of the model with both gut microbiota and human variables is better than the model with just human variables, the independent association of gut microbiota with the disease can be confirmed