## 3 Empirical Results
### Model Construction and Evaluation
In addition to the comparison of the prediction performance from different techniques, we further plotted the decision tree which generated through data mining analysis is shown in Figure 4. The decision tree methods identified variables respectively by its importance degree in the order of comorbidity on renal, patient's age, surgical intervention, gender, comorbidity on cardiovascular disease, and comorbidity on diabetes mellitus. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Ratio** & **Std. Err.** & \(t\) & \(p\)**Value** & **[95\% Conf.** & **Interval**]** \\ \hline Entire study group & & & & & & \\ age & 0.005 & 0.000 & 19.46 & 0.000 & 0.004 & 0.005 \\ gender (male vs. female) & 0.075 & 0.008 & 9.61 & 0.000 & 0.060 & 0.090 \\ number of comorbidity & 0.004 & 0.006 & 0.69 & 0.488 & \(-\)0.008 & 0.016 \\ the year of fracture & \(-\)0.006 & 0.001 & \(-\)4.56 & 0.000 & \(-\)0.008 & \(-\)0.003 \\ surgical intervention & \(-\)1.554 & 0.122 & \(-\)12.74 & 0.000 & \(-\)0.179 & \(-\)0.132 \\ (yes vs. no) & & & & & & \\ \hline Male group & & & & & \\ age & 0.005 & 0.000 & 15.75 & 0.000 & 0.005 & 0.006 \\ number of comorbidity & 0.007 & 0.010 & 0.68 & 0.497 & \(-\)0.013 & \(-\)0.027 \\ the year of fracture & \(-\)0.009 & 0.002 & \(-\)4.59 & 0.000 & \(-\)0.125 & \(-\)0.005 \\ surgical intervention & \(-\)0.141 & \(-\)0.019 & \(-\)7.34 & 0.000 & \(-\)0.178 & \(-\)0.103 \\ \hline Female group & & & & & \\ age & 0.004 & 0.000 & 10.66 & 0.000 & 0.003 & 0.005 \\ number of comorbidity & 0.002 & 0.007 & 0.23 & 0.822 & \(-\)0.012 & 0.016 \\ The year of fracture & \(-\)0.003 & 0.002 & \(-\)1.82 & 0.069 & \(-\)0.006 & 0.000 \\ surgical intervention & \(-\)0.170 & 0.016 & \(-\)10.83 & 0.000 & \(-\)0.201 & \(-\)0.139 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of logistic regression of first-year mortality odds. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{3}{c}{**Logistic Regression Analysis**} & \multicolumn{4}{c}{**Cox PH Regression Analysis**} \\ \hline
**Features** & **OR** & **95\% CI** & \(p\)** & **Features** & **HR** & **95\% CI** & \(p\)** \\ \hline Gender & 0.59 & 0.536–0.649 & Gender & 1.455 & 1.375–1.539 & \\ Age & 0.932 & 0.926–0.937 & Age & 1.047 & 1.044–1.051 & \\ Surgical intervention & 1.763 & 1.523–2.040 & Surgical intervention & 0.682 & 0.631–0.736 & \\ Comorbidity & & & & & \\ DM & 0.678 & 0.603–0.762 & DM & 1.237 & 1.154–1.327 & \\ CV & 1.339 & 1.214–1.476 & CV & 0.876 & 0.825–0.930 & \\ CVA & 1.047 & 0.487–2.252 & 0.905 & CVA & 0.985 & 0.640–1.514 & 0.944 \\ RENAL & 0.159 & 0.071–0.355 & RENAL & 2.911 & 2.267–3.738 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of mortality perdition models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Sensitivity** & **Specificity** & **Accuracy** & **AUC** \\ \hline C4.5 & 0.674 & 0.672 & 0.674 & 0.723 \\ \hline Random Forest & 0.725 & 0.731 & 0.724 & 0.790 \\ \hline SVM & 0.637 & 0.636 & 0.636 & 0.637 \\ \hline MLP & 0.626 & 0.639 & 0.620 & 0.674 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The predictive validity of the hip-fracture mortality model. Figure 4: The decision tree for predicting a mortality risk of hip fracture patients.