Metagenomics AI powered prediction of Inflammatory Bowel Disease and Probiotic Recommendation

Background and ObjectiveThe dysbiosis of human gut microbiome has been increasingly seen to have a relation in the development of autoimmune diseases, with specific microbial signatures having causative association with specific conditions. Inflammatory bowel disease (IBD) is one such autoimmune ailment. This paper proposes a predictive tool that can identify the IBD status of an individual based on the composition of the gut microbiome using machine learning and AI agents driven techniques. The technology can strengthen the suspicion of a potential IBD diagnosis a patient may have based on their gut microbiome profile. MethodsThe tool processes patient gut metagenome using integrated Kneaddata and MetaPhlAn to generate taxonomic profiles. These are fed into an XGBoost classifier to predict IBD or healthy status. Dysbiotic taxa are identified via Z-score and fold change. CrewAI delivers personalized probiotic recommendations based on diagnosis and dysbiosis. ResultsThe tuned XGBoost model achieved 86.6% accuracy. On validation using single ulcerative colitis sample, the tool correctly predicted IBD status but misclassified it as Crohns disease(possibly due to overlapping microbial signatures), identifying Faecalibacterium and Flavonifractor as dysbiotic taxa.The probiotic recommended was Faecalibacterium prausnitzii, backed with reasoning basedon scientific literature. ConclusionsDespite limited validation sample size, the high accuracy , correct IBD detection,dysbiosis analysis and elaborate probiotic recommendation suggest promising potential; further validation needed