# Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
## 4 Results and Performance Evaluation
### Performance by Sparse Coding  
The accuracy of an FCN is high, as it has many nodes and a high depth. Unfortunately, a large number of nodes and a high depth generate a large number of weights and therefore a considerable number of resources are required. A heterogeneous healthcare platform should have high-level accuracy and a fast turnaround time. For this reason, it is necessary to reduce the amount of computation required. A neural network can be expressed by a continuation of matrix operations and can generate multiple approximate values of \"0\" in a weight matrix through repeated learning. Sparse coding is used to reduce multiple weights and thereby decrease the amount of computation. It has been studied for executing deep learning in environments with limited resources, such as mobile, wearable, and embedded environments. There are multiple means of achieving sparse coding, of which the typical ones are the singular value decomposition (SVD) method of removing the weights approximate to \"0\" [32] and a method of changing the neural network structure [33]. A weight that approximates \"0\" influences output less, and sparse coding is applied to reduce the values approximate to \"0\" to save resources [32]. The method of changing the neural network structure replaces the fully connected (FC) layer with a different layer [33] to reduce weights. In a mobile environment, sparse coding can be used to save resources if the loss of accuracy \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**RMSE of Number of Steps**} \\ \hline
**Attribute** & **ns + 0** & **ns + 1** & **ns + 2** \\ \hline Mean & 80.478 & 96.474 & 107.023 \\ \hline Median & 82.897 & 99.501 & 111.051 \\ \hline
1 Quartile & 160.489 & 190.984 & 212.258 \\ \hline
2 Quartile & 120.714 & 150.418 & 170.143 \\ \hline
3 Quartile & 160.529 & 191.019 & 211.879 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMSE of number of steps. is taken into account [34, 35]. In addition to sparse coding, resource distribution methods, such as cloud computing or edge computing, exist for executing deep learning in a mobile environment. These methods require a high cost without influencing the basic computation of deep learning [36]. Sparse coding-based deep learning can obtain additional benefits by reducing the amount of computation in a healthcare platform environment where device performance is limited. The proposed multimodal deep learning model was sparse-coded in each method for performance evaluation [37, 38]. The performance comparison was conducted in terms of accuracy, parameters, turnaround time, and model size. The proposed multimodal deep learning model (MDM), SVD-based multimodal deep learning model (S_MDM), layer replacement-based multimodal deep learning model (D_MDM), and SVD and layer replacement-based multimodal deep learning model (SD_MDM) were compared [39, 40, 41]. In the completely learned RNN-based multimodal deep learning model, sparse coding was applied to remove the weights that approximated to \"0\". Sparse coding is accompanied by a loss of accuracy because of the weight reduction; therefore, weights should be reduced only in an allowable range. Table 7 shows the effect of sparse coding on the performance of the multimodal deep learning models.