# Model Establishment of Cross-Disease Course Prediction Using Transfer Learning
## 4 Experimental Evaluation
### Experimental Results
#### 4.3.2 Comparison of TL-CDC with Other Time Series Models  
Since there is no disease course development prediction model suitable for multivariate and long-term disease data in previous studies, this study will use a model that can handle multi-feature and long-term data to analyze and predict the disease course development matrix of the source disease, and then transfer the parameters trained by it to compare the prediction outcome and model training time between the method of this study and models of the same architecture that use the target disease course development matrix for analysis.  
This section compares the prediction outcome of the best performing LSTM with that of traditional regression, TPA-LSTM [30] and DSANet [31]. As shown in Figures 18-20, it is evident that the prediction outcomes of weight initialization using traditional linear regression, TPA-LSTM, and DSANet tended to be better than that seen using the parameter transfer method, which shows that neither a simple linear regression nor a complex model specifically developed to capture the time series relationship is suitable for analysis in a transfer learning framework. For the data of three different sequence lengths, the MSE of the method in this study was lower than that of the other methods, which means that it can effectively use the disease course data of two related but different diseases to perform cross-disease course prediction analysis under the transfer learning framework.  
Figure 17: MSE of the target disease course prediction of TL-CDC without attention mechanism under k = 4.  
Figure 18: Comparison of TL-CDC and other time series models for predicting the course development of target disease under k = 2.  
This study trained the model with k = 4 using graphic cards NVIDIA GeForce RTX 3090 Ti, 24 GB G6X, and graphics card acceleration software CUDA11.2 and CUDNN8.1.1. Table 7 presents the comparison of model training time. The source disease course prediction representation used LSTM as the training time spent on the source disease course development prediction model. The target disease course prediction representation used the time spent after the source model parameters are transferred to the target model and the time spent on the development of the target disease course prediction model as the training time. As shown in Table 7, it is evident that the prediction outcome of TL-CDC is the best, and the training time of the model is much less than that of most models. In the comparison of the training time of this model, k = 4 is used for comparison, but when k is 2 or 3, the number of sequence data will increase exponentially, causing the training time of the model to increase exponentially. Considering hardware, time cost, and prediction accuracy, TL-CDC is the best option for the prediction of course development across diseases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Source Disease** & **Target Disease** \\ \hline TL-CDC (Ours) & 19.158 min & 1 min \\ Linear Regression & 10.826 min & 0.593 min \\ TPA-LSTM & 99.607 min & 3.82 min \\ DSANet & 6320.214 min & 118.655 min \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training Time of Prediction Model for Disease Course.  
Figure 20: Comparison of TL-CDC and other time series models for predicting the course development of target disease under k = 4.