# Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
## 4 Results and Performance Evaluation  
### Performance of Time Series Prediction  
In a heterogeneous healthcare platform, multiple values for the same variable can be redundantly collected because of the multimodal characteristic of the platform. In this study, we propose a method for solving the redundancy problem through selecting representative values. Each of the RNNs was learned according to the representative value selection method, and their performance was evaluated. As the representative value, the mean, median, and quartile were used. The redundancy of healthcare proof data differed according to the device sets. In data sets B, D, and E, which had considerable redundancy of sleep data and step counts with high time series fluctuation, the data of 50 days (the data of the other 250 days were used for learning) were used to evaluate accuracy. Data set B had five persons, D had four persons, and E had one person. There was a total of 500 transactions. For accuracy, RMSE was used to evaluate the degree of error.  
Table 4 shows the RMSE and representative value of sleep data. In this table, to +0 in Attribute means the value at the current time (+0) for \"roll over in sleep,\" and to +1 represents the value at the next time. Is means light sleep, and ds means deep sleep. The greater the distance of the sleep data prediction from the current time, the larger the error range.  
According to the RMSE evaluation results of the representative value selection method, the difference between the mean and the median is not large. The quartile value was at a greater distance from the center of the data distribution than the mean or median. If it is used as a representative value, the prediction error is high. When the mean or median value was used as a representative value, the difference was not large. Therefore, using this method, it is possible to solve the redundancy problem of multiple devices.  
Table 5 presents the RMSE of the number of steps. In this table, ns + 0 represents the value of the step count at the current time, and ns + 1 represents the value at the next time. The number of steps had a wide range and differed significantly according to the device used to record it. For this reason, the RMSE was high overall. In terms of the number of steps, the difference between the mean and the median values was not large, and the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**RMSE of Sleep Data**} \\ \hline
**Attribute** & **to + 0, 1, 2** & **ls + 0, 1, 2** & **ds + 0, 1, 2** \\ \hline Mean & 1.298 & 14.240 & 8.122 \\ \hline Median & 1.310 & 14.397 & 8.147 \\ \hline
1 Quartile & 2.619 & 17.155 & 9.299 \\ \hline
2 Quartile & 2.376 & 16.487 & 8.802 \\ \hline
3 Quartile & 2.554 & 17.178 & 9.169 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSE and representative value of sleep data.  
Figure 4: Structure of the proposed multimodal deep learning for estimating missing values.  
quartile value showed a high error. In a heterogeneous healthcare platform, in most cases data redundancy occurs two to four times. The mean-based representative value selection method uses the same calculation formula in all situations, regardless of the redundancy count, and therefore it is the most efficient method.