# A Novel Neural Network-Based Method for Medical Text Classification
## 4 Experiments
### Results
#### 4.3.2 Effect of Parameter Tuning  
The classification performance of neural networks is greatly influenced by parameter adjustment, especially the optimization algorithm and function in classifier. In this section, we explore the effects of parameters on the model through experiments.  
In the process of modeling, it is found that the number of hidden units will affect the performance of the model. In order to get better performance, we select the most suitable number of hidden units for our model. We conduct experiments on four datasets with the different number of hidden units. The effect of different number of hidden units on accuracy of the model as show in the Figure 4. The experimental results show that the performance of text classification is best when the number of GRU hidden units is 100 in three datasets. However, when hidden units are 128, CCKS dataset has best accuracy. This result shows that the number of GRU hidden units has a slight influence on the model and the number of GRU hidden units can be variable. Considering comprehensively, we set the number of hidden units to be 100.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **TCM** & **CCKS** & **AIM** & **Hallmarks** \\ \hline CNN & 73.17 & 89.37 & 95.47 & 70.85 \\ LSTM & 60.00 & 80.62 & 57.36 & 72.73 \\ RCNN & 62.03 & 84.38 & 96.06 & 74.76 \\ HAN & 76.62 & 88.75 & 97.30 & 74.70 \\ SVM & 48.78 & 53.00 & 89.93 & 70.34 \\ Fasttext & 80.00 & 75.01 & 90.28 & **80.45** \\ Logistic Regression & 56.50 & 73.12 & 89.84 & 74.24 \\ AC-BiLSTM & 80.51 & 88.12 & 97.92 & 74.97 \\ Our method & **89.09** & **93.75** & **97.73** & 75.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The classification accuracy of the proposed method against other models on four datasets (%).  
The different optimization algorithms and activation functions can also affect the performance of the model. We conduct experiments on four datasets with the different optimization algorithms (RMSProp and Adam [30]) and different activation functions (softmax and sigmoid). Figure 5 left shows the classification effect of two different optimization algorithms (RMSProp and Adam) on four datasets. From the experimental results, we know that the optimization algorithm RMSProp used in our method is better than Adam for the medical record datasets (TCM and CCKS). However, in the medical literature datasets (AIM and Hallmarks), the classification results of the two are basically equal. Combining the results of four datasets, it shows that both Adam and RMSProp can perform well in our model but RMSProp is better.  
Figure 5 right shows the classification effect of two different activation functions (softmax and sigmoid) in the classifier. Softmax and sigmoid are two functions which are commonly used in logistic regression and neural networks. It shows that the classification performance of the sigmoid function in the medical record datasets (TCM and CCKS) far exceeds the softmax function but, in the medical literature datasets (AIM and Hallmark), the classification accuracy is similar. The classification model of HAN which uses the softmax function get optimal results in the general domain. However, in the medical texts the classification performance of sigmoid is better than softmax. From this, it can be known that sigmoid function is more suitable for our method.