## 3 Results
### Comparison of Our Method with Other Existing Methods  
We compared the performance of our proposed method with other existing methods. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) described a method based on a directed gene network using the random forest classifier in 2018. They generated models corresponding to each disease with the weight of the out-degree in the gene directed network including the positive or negative associations between genes and between the drugs and their target genes. An assessment of [PERSON]'s method in predicting drug-disease associations revealed that the random forest classifier produced excellent prediction performance. To compare the performance of our proposed approach with [PERSON]'s method, the same set of drugs, diseases, and known drug-disease pairs were employed as inputs with the total number of diseases set at 460. The percentage of diseases whose proposed method and [PERSON]'s method exhibited a higher performance in terms of their AUC, F1, and ACC scores than specific thresholds are listed in Table 3. For thresholds of 0.5 or more, our proposed model consistently produced acceptable AUC, F1, and ACC scores for 440 or more of the 460 diseases (95.9%), whereas [PERSON]'s method produced acceptable scores for fewer than 440 diseases. Additionally, the results show that higher AUC, F1, and ACC thresholds lead to a higher number of efficient models for the prediction of new drugs for a disease. Figure 4: Verification of candidate drug–disease pairs in terms of their functional similarity using a Wilcoxon rank-sum test. (**a**) Enrichment analysis, (**b**) comparison of the functional modules with known drugs, and (**c**) functional score computations. [PERSON] et al. (2019) proposed a drug-disease associations model in 2019. They generated five meta-paths which are drug-disease matrices based on drug-disease, drug-protein, and disease-protein interaction data, that included reliable negative, i.e., a set of drugs that cannot be used to treat diseases. They applied the singular value decomposition (SVD) technique to extract the latent features for the drugs and diseases. Then, they combined these latent features to represent drug-disease pairs. Random forest classification was employed to generate five models from the five meta-paths. They subsequently applied the ensemble technique to combine all five random forest models. [PERSON]'s approach outputs all candidate drug-disease pairs but our method proposes candidate drugs for each disease. To compare the two approaches, we combined all candidate drugs for all diseases and then randomly selected the non-candidate drugs for each disease with the same number of those candidate drugs corresponding with disease. This produced a set of 1317 drugs, their targets, and 478 diseases for comparison. Table 4 demonstrates that the performance values with 478 common diseases in our proposed model are higher than that of [PERSON]'s method. Thus, the results showed that our model has outperformed that other model. We also analyzed the performance of [PERSON]'s method, [PERSON]'s method, and our proposed method at the same time by combining candidate drugs for all of the diseases in [PERSON]'s method in order to evaluate the same set of drugs, diseases, and known drug-disease pairs. Table 5 shows the performance of the three models for the 460 diseases and 1315 drugs common to all methods. [PERSON]'s method, which is based on a random forest classifier, and our method produced a higher performance, while the area under the precision-recall curve was greater for [PERSON]'s method than for [PERSON]'s method. The performance scores for our method were also higher than those of [PERSON]'s method. Overall, our method dominated the other models based on the common set of diseases and drugs. The ROC curve for the three approaches is presented in Figure 5.