## 4 Discussion
This may be attributable to a delay in receiving medical treatment incurred by patients transported from home. This result is also supported by the extant literature in that it has been found that transportation barriers may play an important role, e.g., creating delayed access to health care may cause extended suffering, tough and exorbitant treatment, and increased morbidity and mortality whereas intervention strategies such as the early detection of diseases and timely treatments can save patients transported from home [30]. Therefore, the reasons behind the poor medical outcomes of RCC patients referred from home require further investigation. The results of the C4.5 decision tree algorithm suggest that by adding the 12 disease attributes to the analysis, the precision improved almost 5% from 74.90% to 79.80%. The recall rate also improved significantly, from 70.80% to 78.80%. The other three performance measurements, namely, F-measure, ROC area, and PRC area, also improved, as shown in Table 6 and Figure 3. These data can assist medical personnel in recognizing the workings of the system for the prognosis of RCC patients. As such, medical personnel can recognize the result and intervene in the structure of the resulting decision tree. Future studies can also be conducted to assist with the interpretation of patient data in order to facilitate high rates of true positive and negative results. [PERSON], et al. (2002) found age to be associated with increased mortality in ventilated patients [31], i.e., ventilator-dependent patients older than 80 usually experience extremely poor medical outcomes. Our study, however, showed that age is not a risk factor related to the medical result of RCC patients; consequently, higher age was not definitely found to be associated with worse health statuses. As such, there may be other factors related to the medical outcomes of RCC patients, and thus, more research is needed to explore the effect of age on the prognosis of RCC patients. As with all research, our study has limitations. One is that the study employed a cross-sectional design. As such, the effect of factors related to RCC patients' medical outcomes needs further examination in that the study sample originated from one metropolitan-regional hospital in central Taiwan, and thus, the findings may not be generalizable to RCC patients throughout Taiwan. Another weakness is the small case number of some categories used to evaluate the relationship between the variables. Despite these weaknesses, our study contributes to the literature in that it explored the relationship between the medical outcomes' distribution and health issues such as APACHE II scores and BMI status levels, as admission sources for RCC patients in Taiwan. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F-Measure** & **ROC Area** & **PRC Area** \\ \hline Model I (5 basic attributes) & 74.90\% & 70.80\% & 69.60\% & 85.10\% & 76.00\% \\ \hline Model II (5 basic + 12 disease attributes) & 79.80\% & 78.80\% & 78.20\% & 89.20\% & 81.70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: C4.5 algorithm performance comparison for Model I and Model II.