## Materials and Methods
### Investigated Classification Techniques  
The data mining techniques selected in this study were support vector machines (SVMs), classification and regression trees (CARTs), C4.5 algorithm, and logistic regression (LGR) [27; 28; 29; 30; 31]. SVM, a supervised learning method for classification, is currently one of the most effective methods for high-dimensional data ([PERSON] and [PERSON], 1995). An SVM initially maps input and output variables to a high-dimensional vector space by using structural risk minimization, which minimizes boundary errors by induction. Then, the SVM seeks a separating hyperplane to divide the data into two or more categories. Therefore, a new instance can be mapped into one of the subspaces projected by a set of hyperplanes, and the majority class in this subspace is assigned to the new instance.  
Decision tree (DT) is a classification technique commonly used in data mining. In the tree, each internal node represents a single IV. Each branch represents one or more possible values of the selected IV, and each leaf-node represents a class label. During tree construction, the DT-based algorithms recursively select an IV to reduce the impurity of the instance group. When the stopping criteria are satisfied, a class label will be assigned to a leaf-node. Both CART and C4.5 are DT-based algorithms, but they have two major differences during the tree-growing phase: (1) the impurity  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Category** & **Variable Name** & **Definition** & **Type** \\ \hline \multirow{10}{*}{Surgery} & K & K & Numerical \\  & GLU & Blood glucose & Numerical \\ \hline \multirow{8}{*}{Surgery} & SURGEON & Surgeon ID & Categorical \\  & OP & Surgery category (ICD-9-CM code) & Categorical \\  & ASA & American Society of Anesthesiologists (ASA) class & Categorical \\  & ANES\_TYPE & Anesthesia type (GA-tube/GA-LM/SA/EA) & Categorical \\  & TU & The use of tourniquet (Yes/No) & Categorical \\  & EM\_SUR & Emergency surgery (Yes/No) & Categorical \\  & OP\_DAYS & (Wait OP days) & Numerical \\ \hline \multirow{8}{*}{History} & LUNG & Whether the patient had lung disease? (Yes/No) & Categorical \\  & CVD & Whether the patient had cardiovascular disease? & Categorical \\  & DM & Whether the patient had diabetes? (Yes/No) & Categorical \\  & HT & Whether the patient had hypertension? (Yes/No) & Categorical \\  & LIVER & Whether the patient had liver disease? (Yes/No) & Categorical \\  & KIDNEY & Whether the patient had renal disease? (Yes/No) & Categorical \\  & SMOKE & Whether the patient had smoke? (Yes/No) & Categorical \\  & ALCOHOL & Whether the patient had alcohol? (Yes/No) & Categorical \\  & ANTI\_COA & Whether the patient had used anticoagulant drug & Categorical \\ \cline{1-1} \cline{2-4}  & & use? (Yes/No) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
measure of CART is the Gini index, while in C4.5, it is the gain ratio; (2) CART method builds binary trees, while C4.5 builds a multiway tree.  
Regression analysis is a statistical learning method; it analyzes the collected data to develop a mathematical model for predicting the output variable. It aims at determining the correlation strength between the input and output variables. LGR is a nonlinear regression model, where the dependent variable is categorical. LGR can be used to predict the probability of an event by fitting the data objects to a logistic function, that is, it allows input variables with any value to be put into the logistic function to obtain a probability value between 0 and 1.