## 3 Methods
### Supervised Learning Techniques  
Four well-known supervised learning techniques were used, including LGR, decision tree (DT), a back-propagation neural network (BPNN), and a support vector machine (SVM). The steps involved in building the models are shown in Figure 1. Both the 103 class-imbalanced data and the 132 class-balanced data were divided into 70% training set and 30% testing set.  
Being a type of multilayer feedforward networks, BPNN is able to deal with non-linear classification, and is the most commonly used supervised learning network [24]. Many studies have demonstrated  
Figure 1: Research Flowchart for Building Prediction Models.  
the application of this network in building prediction models for medical conditions and found the accuracy rates of such models to be satisfactory. The C5.0 DT enables the prediction of future data based on patterns extracted from a series of verification processes using the training data set; application of C5.0 DT in the field of medicine is widely popular for the use of exploring important factors to construct predictive models. SVM is a supervised learning classifier, and its use of RBF kernel has been shown to have better performance than other models in medicine-related studies [25].  
This study used SPSS version 22 to conduct various statistical operations, including performing LGR, selecting input variable features by using raw data with imbalanced categories, using medical record data with balanced categories, and using MRI diagnostic data. Subsequently, the researchers screened through variables with significant influence and determined whether significant differences occurred in the accuracy rate of models constructed before and after screening.  
In this study, a C5.0 DT was used to select features for input variables, delete input variables with a lower influence on target variables, select input variables with substantial influence, and compare whether significant differences exist in terms of the accuracy of models constructed before and after screening.  
This study applied the aforementioned supervised learning techniques to construct a prediction model by back-propagating the factors selected through feature selection procedures as the input variables and dividing them into training and testing groups. For hidden layer configuration, two levels of hidden layers are established using the most commonly used sigmoid function. For learning efficiency, the best parameter setting for the model is identified through trial and error. For the output layer, the decision of whether to perform surgical treatment is determined using two output neurons. For the learning parameters, trial and error is used to identify the best parameter setting. The termination conditions are set for when the network training reached a convergent and completely stable state and when learning efficiency, maximum learning iteration number, and mean square error are present. The BPNN model is illustrated in Figure 2.  
In this study, the researchers input factors selected through feature selection into C5.0 DT as the root nodes and generated child nodes through splitting the data set performed from the maximum. This step was conducted repeatedly until the data set could no longer be split. Then, the rules affecting the final outcome of whether patients who opted for conservative treatment ultimately underwent surgical treatment were formed from the key factors.