## 4 Measurements and Experimental Results
### Evaluation of Model Performance  
The researchers constructed 12 predictive models. The sensitivity, specificity, accuracy, and AUC values obtained using the confusion matrix of each assistive diagnostic model are displayed in Table 4.  
The current study featured a binary problem, and the adopted model performance evaluation methods involved a confusion matrix and AUC. The accuracy rate, sensitivity, and specificity of the models were calculated using the confusion matrix and AUC. The percentage ratio of patients who \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constructed Model** & **Sensitivity** & **Specificity** & **Accuracy rate** & **AUC** \\ \hline LGR+ C5.0 DT & & & & \\ for feature selection & 0.9 & 1 & 94.87 & 0.952 \\ using a BPNN & & & & \\ \hline BPNN & 0.95 & 0.947 & 94.87 & 0.949 \\ \hline C5.0 DT for feature selection & 0.9 & 0.947 & 92.30 & 0.924 \\ using a BPNN & & & & \\ \hline LGR + C5.0 DT & & & & \\ for feature selection & 0.9 & 0.94 & 92.3 & 0.924 \\ using a SVM & & & & \\ \hline LGR + C5.0 DT & & & & \\ for feature selection & 0.9 & 0.94 & 92.3 & 0.924 \\ using a C5.0 DT & & & & \\ \hline LGR for feature selection & & & & \\ using a BPNN & 0.8 & 1 & 89.74 & 0.913 \\ \hline SVM & 0.8 & 1 & 89.74 & 0.913 \\ \hline C5.0 DT & 1 & 0.8 & 89.74 & 0.910 \\ \hline C5.0 DT for feature selection & & & & \\ a SVM & 0.8 & 0.947 & 87.17 & 0.880 \\ \hline LGR for feature selection & & & & \\ using a SVM & 0.8 & 0.947 & 87.17 & 0.880 \\ \hline C5.0 DT for feature selection & & & & \\ using a C5.0 DT & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of each assistive diagnostic model. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Feature Selection Method** & \\ \hline LGR & C5.0 DT & LGR and C5.0 DT \\ Gender & Gender & Gender \\ Age & Age & Age \\ Education & - & Education \\ Systolic pressure & - & Systolic pressure \\ Diastolic pressure & Diastolic pressure & Diastolic pressure \\ Reduction of intervertebral disc & Reduction of intervertebral disc & Reduction of intervertebral disc \\ height & height & height \\ Leg pain & Leg pain & Leg pain \\ Joint pain & - & Joint pain \\ Muscle pain & Muscle pain & Muscle pain \\ - & Muscle weakness & Muscle weakness \\ Whether & Whether & Whether \\ patient has other medical history & patient has other medical history \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compilation table of the three feature extraction methods. needed and did not need to undergo surgical treatment could be accurately predicted by calculating the sensitivity and specificity. Regarding the evaluation performance of the constructed models, the model combining LGR, C5.0 DT for feature selection and using a BPNN exhibited the best model performance among the 12 models constructed. This model has a testing accuracy rate of 94.87%, a sensitivity of 0.9, a specificity of 1, and an AUC of 0.952. The prediction model that combines LGR and C5.0 DT for feature selection exhibited the best model performance among all constructed models. Among the constructed models, the prediction model combining LGR, C5.0 DT for feature selection, and using a BPNN exhibited the best performance. Therefore, this model was used as a basis to assess rules associated with evaluating if an operation was ultimately performed. Seven rules were obtained from this model, as illustrated in Figure 3. Among the seven rules, four rules could be used to evaluate whether an operation was ultimately performed:  
* If no reduction of the intervertebral disc height occurred and the patient was older than 77 years, the patient ultimately underwent the operation. * If a reduction of the intervertebral disc height occurred and the patient's blood pressure difference was less than 60 mm/Hg, the patient ultimately underwent the operation.