Other algorithms used on this dataset also performed at high accuracies, but we found that the HT-based model was most optimal across all statistics. Table illustrates the performance of other models, all with cross-validation of five folds. The HT algorithm shown at the top of Table is the model we chose. Figure shows the accuracy of all the different models, including those that were less optimal. In addition, we verified that HT-based ML model was most optimal based on the number of miRNAs that were provided for training and the number of attributes that were in the model after attribute selection. Both factors affected the accuracy of classification, since the number of miRNAs defined how much data the model had for training, and the number of attributes affected which biological characteristics were determined to be most important in classification. Figure shows a comparison of the highest-performing model accuracy with different numbers of miRNA being used in training and different numbers of attributes being used for classification.