Fig. 5

The decision tree visualization for predicting the survival of serous ovarian cancer. In the prediction processing, at the root node, the sample is divided into two groups which have the MO/LY value less or equal to 0.315, or not. Then, the divided samples need to be judged by the second layer leaf node. In the second layer leaf nodes, the value CA125 or differentiation are the standards of classification. After that, it will go through into the third layer of leaf nodes until there is no leaf node left. Finally, when the decision reaches the last leaf node, the survival probability is the number of class samples divide total samples in the node. For example, at the leftmost leaf node, the probability of survival is 5/6 and the probability of death is 1/6