A machine learning classifier that diagnoses autism spectrum disorder (ASD) is described that transforms data obtained from a patient medical history and a patients saliva into data that correspond to a test panel of features, the data for the features including human microtranscriptome and microbial transcriptome data, wherein the transcriptome data are associated with respective RNA categories for ASD. The classifier classifies the transformed data by applying the data to the classifier that has been trained to detect ASD using training data associated with the features of the test panel. The trained classifier includes vectors that define a classification boundary and predicts a probability of ASD based on results of the classifying.