## 3 Results
### Experimental Results  
In experimental evaluations, six binary classification models were developed to identify high risk patients. The experimental results are reported in Table 6. AUC, F-measure, precision, and recall were used to assess the performance of the prediction models.  
Regarding the AUC values ranged from 0.551-0.836. The results showed that the performance of RF classifier was the best (0.836; excellent discrimination), followed by C4.5 (0.719) and ANN (0.688); the remaining classifiers performed poorly. Figure 1 illustrates the receiver operating characteristic (ROC) curve of RF classifier. In addition, F-measure is the harmonic mean of precision and recall, so it is commonly adopted to compare the whole performance. Regarding the F-measure of the prediction models, the RF result was still the best (0.794), followed by C4.5 (0.767) and ANN (0.734); the other  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **AUC** & **Precision** & **Recall** & **F-Measure** \\ \hline \(k\)NN & 0.569 & 0.722 & 0.691 & 0.706 \\ C4.5 & 0.719 & 0.803 & 0.734 & 0.767 \\ SVM & 0.554 & 0.698 & 0.536 & 0.607 \\ RF & 0.836 & 0.852 & 0.742 & 0.794 \\ ANN & 0.688 & 0.795 & 0.682 & 0.734 \\ LGR & 0.551 & 0.689 & 0.532 & 0.600 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of classification performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Name** & **Range** & **Unit** & **Summary Statistics** \\ \hline \multirow{8}{*}{IV} & \multirow{4}{*}{Demographic} & \multirow{4}{*}{Gender} & \multirow{4}{*}{Male/female} & \multirow{4}{*}{-} & Male: 278 (54.19\%) \\  & & & & & Female: 235 (45.81\%) \\  & & Age & 19 to 101 & years & \(\mu=74.03\), \(\sigma=12.08\) \\  & & Weight & 32 to 105 & kg & \(\mu=56.26\), \(\sigma=12.01\) \\  & & ALT & 5 to 1754 & U/L & \(\mu=78.33\), \(\sigma=161.58\) \\  & & AST & 11 to 2615 & U/L & \(\mu=88.25\), \(\sigma=215.26\) \\  & & SCT & 0.27 to 12.1 & mg/dL & \(\mu=1.46\), \(\sigma=1.387\) \\  & & BUN & 2 to 237 & mg/dL & \(\mu=34.33\), \(\sigma=28.53\) \\  & & ALB & 1 to 4.3 & g/dL & \(\mu=2.625\), \(\sigma=0.639\) \\  & & K+ & 1.9 to 6.7 & meog/L & \(\mu=3.987\), \(\sigma=0.726\) \\  & & \multirow{4}{*}{History} & CHF & Yes/no & - & Yes: 278 (54.19\%) \\  & & & & & No: 235 (45.81\%) \\ \multirow{4}{*}{} & \multirow{4}{*}{Medication} & \multirow{4}{*}{Dose} & \multirow{4}{*}{0.125 to 2} & \multirow{4}{*}{mg} & \(\mu=0.504\), \(\sigma=0.223\) \\  & & DDI & Yes/no & - & Yes: 364 (0.96\%) \\ \hline \multirow{2}{*}{DV} & \multirow{2}{*}{Treatment outcome} & \multirow{2}{*}{Risk} & \multirow{2}{*}{Low/high} & \multirow{2}{*}{-} & No: 149 (29.04\%) \\  & & & & & High: 344 (67.06\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical information for the collected cases.  
classifiers performed poorly. Overall, the decision tree-based methods including RF and C4.5 exhibited markedly superior prediction performance in our study.  
To understand the performance of the model in more specific realistic scenarios, we further adopted the best classifier RF to investigate the changes when controlling for specific variables, including gender, CHF and DDI. The experimental results are shown in Table 7. The highest AUC value was 0.848 in the female and patients with DDI datasets; the model for patients without DDI was the only one that yielded the result below 0.8.