Machine Learning Decodes the Microbiome: Advancing Diagnostic Accuracy in Gastrointestinal Diseases

Abstract

[Abstract]: This study evaluates the diagnostic accuracy of machine learning (ML) models on gut microbiome data for gastrointestinal diseases. By assessing batch correction and profiling strategies, the results show that species-level taxonomic features excel for Crohn's disease, whereas ComBat-seq batch correction improves predictive accuracy in colorectal cancer. Algorithms like Random Forest and XGBoost proved most effective, offering guidance for reproducible non-invasive diagnostics.