To prepare the ML model using miRNA descriptors for diagnostics of laryngeal cancer (LC) we prepared two sets of miRNAs. The first set was selected from public sources of miRNA-related to LC, and the second set included random miRNA extracted from the miRBase . Then we created a strategy to generate attributes for these miRNAs. Our strategy was three-fold: (1) generate sequence-based attributes for each miRNA's sequence, (2) generate a list of predicted target genes for each miRNA, (3) generate a list of predicted pathway processes in which are involved genes that are targets of selected miRNA. Such a list of attributes was generated for each miRNA. This data was used for training of the ML model. Before generating any other attributes, we created a separate attribute that categorized classification-we assigned a 1 to all LC associated miRNA and a 0 to all randomly associated miRNA. Our model was trained to classify miRNA based on this attribute.