# Model Establishment of Cross-Disease Course Prediction Using Transfer Learning
## 4 Experimental Evaluation
### Experimental Results
#### 4.3.1 Internal Experimental Results  
This section will discuss the prediction outcome of this research method. It will first discuss the prediction effectiveness of transfer learning, and then explain the impact of the parameter freezing and fine-tuning methods of transfer learning on disease course prediction, and then compare the effectiveness of adding attention mechanism calculation to disease course prediction. Figures 9-11 show the time series models of the source disease course prediction model under various \(k\). Meanwhile, \"frozen\" represents freezing the parameters trained by the time series model in the source disease course development prediction model, and then transferring the parameters to predict the target disease course development prediction model. This means that the parameters from the source disease course development prediction model will not participate in the back-propagation calculation of the target disease course development prediction task training. Meanwhile, \"retraining\" indicates the training of weight initialization, which means that only the disease course representation matrix of the target disease is used for predictive analysis of disease course development. For the transferred parameters, we focus on the difference between frozen and fine-tuning in the prediction outcome of the target disease course prediction model. Regarding fine-tuning, this study will discuss the influence that 10% and 50% parameters of participating in back-propagation calculation will have on the prediction outcome. '10% Fine-tuning' and '50% Fine-tuning' represents the fine-tuning method with 10% and 50% parameters, respectively. Figure 9: MSE of the source disease course prediction of TL-CDC under k = 2. As shown in Figures 9-11, it is found that using the target disease data to partially fine-tune the transferred parameters can effectively improve the prediction outcome, especially using parameter transfer on LSTM and Bi-LSTM. Irrespective of whether an attention mechanism was added to the prediction model of the source disease course development, the use of the fine-tuning method for parameter transfer can improve the outcome. The outcome of parameter transfer based on GRU with k = 3, 4 is not significantly improved. We believe that when the data content of the sequence increases and when fine-tuning training is performed for the target disease prediction task, GRU, a time series model with a simpler memory unit, cannot efficiently retain the parameters that are transferred from the source disease prediction model and that are also helpful for the target disease prediction task, resulting in no improvement in the GRU/s outcome with the fine-tuning method. The prediction effect of adding the attention mechanism is compared in the following figures, in which Figures 12-14 indicate the attention mechanism being transferred from source task to target task prediction, and Figures 15-17 indicate target task without attention mechanism. Figures 12-14 present the prediction outcome of the attention mechanism on the target task. Combining the parameter transfer with the attention mechanism for the target task can significantly improve the prediction outcome of the target task. From the perspective of the three time series models, the lowest MSE with k = 2 is calculated by adding the attention mechanism to the target task prediction, and the prediction outcome of LSTM is the best. The best prediction outcome when using the two time series models,  
Figure 11: MSE of the source disease course prediction of TL-CDC under k = 4. Figure 10: MSE of the source disease course prediction of TL-CDC under k = 3. Bi-LSTM and GRU, was achieved on the weight initialization model. The method proposed in this study can achieve the application of cross-disease prediction of disease course development while having a similar prediction outcome.