## 3 Results
### The Medical Outcomes and Related Factors
#### 3.1.3 C4.5 Decision Tree Algorithm  
Two different models were built using different attributes as the input for the C4.5 algorithm (i.e., the patients' medical data), and the experimental results were derived using 10-fold cross validation. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Medical Outcome**} & & \\ \hline \multicolumn{4}{c}{\(n\) (\%)} & \\ \hline \multicolumn{4}{c}{**Critical or Decased**} & **RCW** & **Weaning** & **Total** & \(p\) **Value** \\ \hline Yes & 4 (26.7) & 1 (6.7) & 10 (66.7) & 15 (100) & \\ No & 57 (25.8) & 61 (27.6) & 103 (46.6) & 221 (100) & \\ Dementia & & & & \\ Yes & 6 (23.1) & 9 (34.6) & 11 (42.3) & 26 (100) & \\ No & 55 (26.2) & 53 (25.2) & 102 (48.6) & 210 (100) & \\ Parkinson’s disease & & & & & \\ Yes & 0 (0) & 5 (38.5) & 8 (61.5) & 13 (100) & \\ No & 61 (27.4) & 57 (25.6) & 105 (47.7) & 223 (100) & \\ Miscellaneous & & & & & \\ Yes & 9 (37.5) & 6 (25.0) & 9 (37.5) & 24 (100) & \\ No & 52 (24.5) & 56 (26.4) & 104 (49.1) & 212 (100) & \\ \hline \hline \end{tabular}  
*: \(p\) \(<\) 0.05.  
\end{table} 
Table 2: \(Cont.\) \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **OR** & **95\% C.I.** & \(p\) **Value** \\ \hline \multicolumn{4}{c}{Medical Outcomes} \\ Critical or deceased & Reference & & \\ RCW & 8.092 & 4.092–16.000 & \textless{}0.001 \\ Weaning & 2.200 & 1.171–4.133 & 0.014 \\ \hline \multicolumn{4}{c}{Diabetes} \\ No & Reference & & \\ Yes & 0.622 & 0.372–1.044 & 0.072 \\ \hline \multicolumn{4}{c}{BMI} \\ Normal weight & Reference & & \\ Under weight & 0.779 & 0.372–1.630 & 0.507 \\ Over weight & 1.198 & 0.587–2.445 & 0.620 \\ Obese * & 2.426 & 1.106–5.318 & 0.027 \\ \hline \multicolumn{4}{c}{Source of admission} \\ Home ** & 2.104 & 1.257–3.523 & 0.005 \\ Long-term care & Reference & & \\ institution & & & \\ \hline \multicolumn{4}{c}{Apache II scores} \\ \textless{}15 & Reference & & \\
15–24 & 0.291 & 0.758–2.524 & 0.291 \\ \(\geq\)25 ** & 2.640 & 1.283–5.433 & 0.008 \\ \hline \multicolumn{4}{c}{R\({}^{2}\) = 0.116} \\ \hline \hline \end{tabular}  
*: \(p\) \(<\) 0.05, **: \(p\) \(<\) 0.01.  
\end{table} 
Table 3: The ordinal logistic regression analysis of medical outcomes of patients in the RCC. For Model I, five basic attributes were selected based on the statistical analysis. These included age, APACHE II score, move status, gender, and BMI value. Table 4 and Figure 1 show the performance of the C4.5 decision tree algorithm using these five basic attributes. The results included the following: precision (74.90%), recall (70.80%), F-measure (69.60%), ROC area (85.10%), and PRC area (76%). The decision tree reflects inferential statistical outcomes illustrated above very well. It additionally reveals that younger patients tend to fall into a class with a high probability of successfully weaning. For Model II, in addition to the five basic attributes adopted in Model I, 12 disease-related factors were added as input for the C4.5 decision tree algorithm. These included cancer, cardiac disease, pneumonia, cerebrovascular accident, diabetes, lower respiratory illnesses, hypertension, chronic kidney disease (CKD), liver disease, dementia, Parkinson's disease, and other chronic diseases. Table 5 and Figure 2 show the performance of the C4.5 decision tree algorithm with its 5 basic and 12 disease attributes. The results of the C4.5 algorithm improved as follows: precision (79.80%), recall (78.80%), F-measure (78.20%), ROC area (89.20%), and PRC area (81.70%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F-Measure** & **ROC Area** & **PRC Area** \\ \hline Weaning & 64.40\% & 91.20\% & 75.50\% & 83.70\% & 80.40\% \\ \hline Critical Death & 81.40\% & 57.40\% & 67.30\% & 88.80\% & 74.00\% \\ \hline RCW & 87.90\% & 46.80\% & 61.10\% & 84.00\% & 70.10\% \\ \hline Weighted Avg. & 74.90\% & 70.80\% & 69.60\% & 85.10\% & 76.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: C4.5 algorithm performance for Model I with five basic attributes.