The microbiome, the community of microorganisms living within an individual, is a promising avenue for developing non-invasive methods for disease screening and diagnosis. Here, we utilize 5643 aggregated, annotated whole-community metagenomes to implement the first multiclass microbiome disease classifier of this scale, able to discriminate between 18 different diseases and healthy. We compared three different machine learning models: random forests, deep neural nets, and a novel graph

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... ional architecture which exploits the graph structure of phylogenetic trees as its input. We show that the graph convolutional model outperforms deep neural nets in terms of accuracy (achieving 75% average test-set accuracy), receiver-operator-characteristics (92.1% average area-under-ROC (AUC)), and precision-recall (50% average area-under-precision-recall (AUPR)). Additionally, the convolutional net's performance complements that of the random forest, showing a lower propensity for Type-I errors (false-positives) while the random forest makes less Type-II errors (false-negatives). Lastly, we are able to achieve over 90% average top-3 accuracy across all of our models. Together, these results indicate that there are predictive, disease-specific signatures across microbiomes that can be used for diagnostic purposes.