## 2 Materials and Methods
### Description of DT Algorithms  
[PERSON] [59] developed the CHAID that is known as a data mining algorithm. Then, a DT model was grown by CHAID through performing a number of sequential combinations and splits on the basis of the Chi-Square test. In fact, CHAID is a tree-based structure technique that has been made by combining a group of rule classifiers. In this technique, for predicting the independent parameter, there is need to form a tree structure of the dependent parameters. The accuracy level of the rules formed in the previous step is denoted by the ratio of records holding the specific value for the target variable to the values given for the independent variables. Another CHAID objective is the generation of wider and non-binary trees. In addition, for the overfitting avoidance purpose, this algorithm prunes DT in an automatic way.  
[PERSON] [60] developed the CART model as a binary DT applicable to modelling purposes. The CART objective is exploring the optimal split. In case of different variables types, CART can be used to classify the input variables values ordering from smallest to largest ones. This technique makes a trial split in aiming at determining the best split point; for example, in case the call split point is termed \"S\", all the cases with a value smaller than \"S\" will go to the left, which forms the child node (X < S); otherwise, the instances will be sent to the right child node (X > S). CART makes attempts to decrease the leaf nodes' impurity level in such a way that the best data partition could be selected. In CART, three potential indices are taken into account to choose the best data partition: Gini criterion entropy, and Twoing criterion. The selection of the above-noted indices has a significant effect on the performance of CART. In other words, selecting the best indices will result in achieving the best predictive performance.  
The QUEST was developed by [PERSON] and [PERSON] [61]. This algorithm addresses variable selection bias issues, particularly the exhaustive-search method that is used in CART. The QUEST selects each splitting variable and its associated split value sequentially instead of simultaneously. This algorithm employs several statistical tests, including overall level of significance, p-value, and F-test to determine the splitting variable at a particular node.  
The C5 algorithm is an improved and commercialized version of C4.5 which was developed by [PERSON] [62]. C4.5 can be used for both continuous and categorical target variables. This algorithm creates binary splits on continuous variables and multiway splits on categorical variables. Additionally, a heuristic searching strategy is employed by C4.5 for variable splitting. Regarding the splitting criterion, the C4.5 uses an impurity measure, such as gain ratio. A C4.5 tree size is controlled using a pessimistic pruning strategy. Finally, the missing data in C4.5 are handled by adding a new level to the tree. While the C5 inherits the characteristics of the C4.5, this algorithm offers several enhancements to C4.5, such as the ability to specify unequal misclassification costs, the application of fuzzy splits on continuous variables, and boosting trees.