# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 4 Experiments
### Experimental Results  
Tables 4 and 5 summarizes the detailed experimental results according to Macro-F and Macro-AUC. For Macro-F, the results in Tables 4 and 5 can be concluded as follows: (1) When decision tree was applied as the binary classifier, COCOA-RE significantly outperformed the comparable approach without COCOA (SMOTE-EN) by 21%. Compared to algorithms related to COCOA, COCOA-RE not only outperformed COCOA-DT that used a general (decision tree) classifier as the multi-class classifier by 13.4%, but it also outperformed the algorithms using an ensemble classifier as the multi-class classifier, such as COCOA-Ada and COCOA-SAMME. (2) When neural network was applied as the binary classifier, COCOA-RE significantly outperformed the comparable approach without COCOA (SMOTE-EN) by 21.6%. Compared to algorithms related to COCOA, COCOA-RE not only outperformed COCOA-DT that used a general classifier (neural network) as the multi-class \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Results**} & \multicolumn{5}{c}{**The Binary Classifier Is Decision Tree**} \\ \cline{2-6}  & **SMOTE-EN** & **COCOA-DT** & **COCOA-Ada** & **COCOA-SAMME** & **COCOA-RE** \\ \hline Macro-F & 0.384 & 0.410 & 0.437 & 0.457 & **0.465** \\ Macro-AUC & 0.613 & 0.632 & 0.645 & 0.666 & **0.670** \\ \hline \hline \end{tabular} Note: The bold values are best among the results.  
\end{table} 
Table 4: The experimental results when the binary classifier is decision tree. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Results**} & \multicolumn{5}{c}{**The Binary Classifier Is Neural Network**} \\ \cline{2-6}  & **SMOTE-EN** & **COCOA-DT** & **COCOA-Ada** & **COCOA-SAMME** & **COCOA-RE** \\ \hline Macro-F & 0.392 & 0.412 & 0.441 & 0.464 & **0.477** \\ Macro-AUC & 0.620 & 0.646 & 0.654 & 0.660 & **0.671** \\ \hline \hline \end{tabular} Note: The bold values are best among the results.  
\end{table} 
Table 5: The experimental results when the binary classifier is neural network. classifier by 15.8%, but it also outperformed COCOA-Ada and COCOA-SAMME. These results illustrate that COCOA-RE is capable of achieving good balance between precision and recall in learning the class-imbalanced multi-label data set. For Macro-AUC, the results in Tables 4 and 5 can be concluded as follows: (1) When decision tree was applied as the binary classifier, COCOA-RE significantly outperformed the comparable approach without COCOA (SMOTE-EN) by 9.3%. Compared to algorithms related to COCOA, COCOA-RE not only outperformed COCOA-DT by 6%, but it also outperformed COCOA-Ada and COCOA-SAMME. (2) When the neural network was applied as the binary classifier, COCOA-RE significantly outperformed the comparable approach without COCOA (SMOTE-EN) by 8%. Compared to algorithms related to COCOA, COCOA-RE not only outperformed COCOA-DT that used a general classifier (neural network) as the multi-class classifier by 3.7%, but it also outperformed COCOA-Ada and COCOA-SAMME. These results demonstrate the real-value function in COCOA-RE is capable of achieving better performance than reasonable predictive confidence. To further investigate the performance of COCOA-RE in different imbalance ratios, the performance of each approach in each class label was collected based on F-measure. In the case that algorithm A was compared with algorithm B, \(A_{q}\) denoted the performance of algorithm A in class label \(q\) and \(B_{q}\) denoted that of algorithm B in class label \(q\). The corresponding percentage of performance gain was calculated as \(PG_{q}=[\left(A_{q}-B_{q}\right)/B_{q}]*100\%\) that reflected the relative performance between algorithm A and algorithm B in class label \(q\). Figure 2 demonstrates the performance gain \(PG_{q}\) changes along the imbalance ratio of the class label \(q\). As shown in Figure 2, irrespective of whether the binary classifier was decision tree or neural network, each algorithm based on COCOA achieved good performance against SMOTE-EN across all labels, with each \(PG_{q}\) hardly coming below 0.