Predicting autism spectrum disorder through the gut microbiota composition using machine learning.

ABSTRACT: The human gut microbiota plays a crucial role in overall health, impacting various diseases, including autism spectrum disorder (ASD). This study explores the relationship between gut microbiota changes and ASD by analyzing microbial compositions and abundances of 692 gut microbiota samples using public 16S rRNA sequencing datasets. Data preprocessing included normalization and redundancy reduction, retaining 367 microbial features. A machine learning model was then developed to predict ASD, utilizing feature-selected random forest algorithms that showed superior performance in both training and independent test sets. Identified microbial features with high correlation to ASD included Clostridiales bacterium VE202-08, Solobacterium moorei gene, and other features. The finding

SUBMITTER: Li D 

PROVIDER: S-EPMC12765537 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature