doing so, they use mathematical algorithms (e.g., informa- tion gain, Gini index, and chi-squared test) to identify a variable and corresponding threshold for the variable that splits the input observation into two or more subgroups. This step is repeated at each leaf node until the complete tree is constructed. The objective of the splitting algorithm is to find a variable-threshold pair that maximizes the homogeneity (order) of the resulting two or more sub- groups of samples. The most commonly used mathematical algorithm for splitting includes Entropy based information gain (used in ID3, C4.5, C5), Gini index (used in CART), and chi-squared test (used in CHAID). Based on the favorable prediction results we have obtained from the prelimin- ary runs, in this study we chose to use CART algorithm as our decision tree method.