Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks
Table 2
Evaluation of auROC and MCC for the proposed LSTM model versus baseline models.
In this table, we evaluated six classifiers; LSTM, HMM, MLPNN, RF, SVM, and LASSO. For each classifier, we evaluated four types of input features; latent features which extracted from the trained autoencoder, 25 features selected by mRMR method, 25 most variable features, and 215 raw taxonomic profile features. The auROC and MCC results shown below are the average of auROC and MCC measured on the test set. The experiments were repeated 10 times and samples were shuffled after each 10-fold cross-validation to test the robustness of each classifier. P-values were calculated using Mann-Whitney U test between LSTM-mRMR-25 versus each corresponding method.