## 3 Empirical Results
### Model Construction and Evaluation  
Table 4 shows the results of a logistic regression on mortality related to age, gender, comorbidity number, year of fracture and surgical or conservative treatment. In this study group, mortality significantly increased with age (\(p<0.001\)) and male gender (\(p<0.001\)). Fractures treated surgically versus those treated conservatively (\(p<0.001\)) and fractures occurring later in the year (\(p<0.001\)) had significantly lower first-year mortality. Table 5 shows the results of the logistic regression analysis and Cox PH regression analysis. Logistic regression revealed that hip fractures patients' gender, age and they have comorbidity (including diabetes mellitus (DM), cardiovascular (CV), and renal disease) increase in the risk of mortality (odds ratio = 0.678, 1.339, 0.159). Meanwhile, the patients with adopting surgical intervention were less likely to mortality compared to patients who did not (OR = 1.763). Cox PH regression results, similar to the logistic regression analysis results, also showed the patients with DM, CV, and renal comorbidity would increase mortality compared to patients without these comorbidities. Gender, age of patients, and surgical intervention were also consistent with the results of the logistic regression analysis. Figure 3: First year mortality of elder hip fracture patient across years. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Year** & **2000** & **2001** & **2002** & **2003** & **2004** & **2005** & **2006** & **2007** & **2008** & **2009** & **2010** & **Total** & **Trend \(p\) (\({}^{\ast}\) \(t\)-Test \({}^{\ast\ast}\))** \\ \hline All hip fractures & 759 & 601 & 613 & 674 & 638 & 683 & 603 & 692 & 733 & 703 & 739 & 7438 & \\ \hline survival & 665 & 472 & 491 & 547 & 515 & 561 & 504 & 571 & 619 & 605 & 628 & 6178 & \\ \hline death & 94 & 129 & 122 & 127 & 123 & 122 & 99 & 121 & 114 & 98 & 111 & 1260 & \\ \hline First year mortality rate & 12.38\% & 21.46\% & 19.90\% & 18.84\% & 19.28\% & 17.86\% & 16.42\% & 17.49\% & 15.55\% & 13.94\% & 15.02\% & 16.94\% & 0.042 \\ \hline Male hip fractures & 219 & 249 & 252 & 274 & 276 & 270 & 240 & 260 & 285 & 271 & 301 & 2678 & \\ \hline survival & 170 & 176 & 192 & 206 & 210 & 210 & 191 & 207 & 229 & 222 & 249 & 2092 & \\ \hline death & 49 & 73 & 60 & 68 & 66 & 60 & 49 & 53 & 56 & 49 & 52 & 586 & \\ \hline First year mortality rate & 22.37\% & 29.32\% & 23.81\% & 24.82\% & 23.91\% & 22.22\% & 20.42\% & 20.83\% & 19.65\% & 18.08\% & 17.28\% & 21.88\% & 0.000 \\ \hline Female hip fractures & 540 & 382 & 361 & 400 & 362 & 413 & 363 & 432 & 448 & 432 & 438 & 4541 & \\ \hline survival & 495 & 296 & 299 & 341 & 305 & 351 & 313 & 364 & 390 & 383 & 379 & 3916 & \\ \hline death & 45 & 56 & 62 & 59 & 57 & 62 & 50 & 68 & 58 & 49 & 59 & 580 & \\ \hline First year mortality rate & 8.33\% & 15.91\% & 17.71\% & 14.75\% & 15.75\% & 15.01\% & 13.77\% & 15.74\% & 12.95\% & 11.34\% & 13.47\%\% & 12.77\% & 0.769 \\ \hline \end{tabular}  
* Chi-square test for trend. ** +-test for first year mortality rate between male and female group.  
\end{table} 
Table 3: First year mortality following hip fracture from 2000 to 2010. Table 6 presents the predicative performance of the four models developed by data mining algorithms in this study. Across all predictive indices, random forest had the highest predictive performance. According to comparisons of predicting performance among all selected classifiers, we can conclude that the decision tree based classifiers (C4.5, RF) out-perform the functional based classifiers (SVM, MLP), showing that decision tree-based approaches can make a better prediction on the mortality of the patients with hip fracture.