Leveraging explainable AI for gut microbiome-based colorectal cancer classification
Ryza Rynazal, Kota Fujisawa, Hirotsugu Shiroma, Felix Salim, Sayaka Mizutani, Satoshi Shiba, Shinichi Yachida, Takuji Yamada
Abstract
Studies have shown a link between colorectal cancer (CRC) and gut microbiome compositions. In these studies, machine learning is used to infer CRC biomarkers using global explanation methods. While these methods allow the identification of bacteria generally correlated with CRC, they fail to recognize species that are only influential for some individuals. In this study, we investigate the potential of Shapley Additive Explanations (SHAP) for a more personalized CRC biomarker identification. Analyses of five independent datasets show that this method can even separate CRC subjects into subgroups with distinct CRC probabilities and bacterial biomarkers.