## 3 Results  
From the blood bank databases and medical records of the study hospital, 1698 blood preparation records were obtained for patients who underwent an orthopedic surgery. Filtering and preprocessing the data to remove records with missing values or data errors yielded 1396 records for subsequent analyses, which included 661 clinical cases with an intraoperative blood transfusion as well as 735 cases without an intraoperative blood transfusion.  
In Dataset A, the four significant IVs were first tested on the SVM, C4.5, CART, and LGR classifiers. The results in Table 3 reveal that the accuracy of the LGR classifier was the highest (71.80%), followed by the CART classifier (71.10%), and the C4.5 classifier had the lowest accuracy rate (70.30%). The AUC results revealed that the LGR had the highest accuracy (77.40%), while the SVM classifier had the lowest accuracy (70.30%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Predicted Class**} \\ \cline{2-4}  & **Blood Transfusion** & **No Blood Transfusion** \\ \hline \multirow{2}{*}{**Actual Class**} & **Blood Transfusion** & _TP_ & _FN_ \\ \cline{2-4}  & **No Blood Transfusion** & _FP_ & _TN_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variable definition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline SVM & 70.8\% & 78.4\% & 62.3\% & 70.3\% \\ C4.5 & 70.3\% & 69.0\% & 71.7\% & 72.3\% \\ CART & 71.1\% & 71.4\% & 70.7\% & 74.1\% \\ LGR & 71.8\% & 79.2\% & 63.7\% & 77.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results for Dataset A. AUC: area under the receiver operating characteristic curve; SVM: support vector machine; CART: classification and regression tree; LGR: logistic regression.  
In Dataset B, the 13 significant IVs were processed using the same four classifiers. The results in Table 4 show that the prediction accuracy of the CART classifier was the highest (71.80%), followed by LGR (71.70%), and the SVM classifier was the lowest (71.10%). The AUC results showed that the optimal performance was exhibited by the LGR classifier (78.30%), while the least favorable performance was by the SVM classifier (70.80%).  
Finally, in Dataset C, the 12 significant IVs were analyzed using the same four classifiers. The analysis results in Table 5 show that the highest accuracy was obtained by the CART classifier (73.10%), followed by the LGR classifier (72.20%) and C4.5 classifiers (72.20%), and the lowest was for the SVM classifier (71.10%). The AUC results showed that the optimal performance was obtained by the LGR classifier (78.70%), while the least one was obtained by the SVM classifier (70.70%).  
Results showed that the LGR classifier was the optimal classifier and yielded the highest prediction accuracy for all three datasets. In addition, the variables in Dataset C exhibited a relatively satisfactory prediction model performance, indicating that the combination of the CfsSubsetEval module and the GeneticSearch method can generate higher prediction accuracy. However, the prediction model generated from Dataset C must be performed based on 12 IVs. If data for all 12 variables cannot be obtained in clinical practice, Dataset A can be used alternatively because it still produces satisfactory prediction accuracy (77.40%) with only four IVs (OP, BMI, HB, and PLT).