# Creating a Diagnostic Assistance System for Diseases in Kampo Medicine
## 4 Discussion  
The correct answers were assigned a value from 0 to 4, with adjacent values having close meanings. Therefore, this method yielded good diagnostic performance when the MAE was less than 1. Table 5 shows that the highest accuracy was achieved by using the average assessment of the four Kampo doctors as the correct value. As shown in Table 6, RMSE also showed high accuracy. This can be attributed to the fact that taking the average suppressed the degree of error and lessened the subjectivity. Also shown in Table 6 is the accuracy of the evaluation using RMSE (root mean squared error), with the average value of the evaluation of the four Kampo doctors as the correct value. As in Table 5, it was shown that the accuracy was improved by modulation and high accuracy was obtained. The distribution of the ratings of the Kampo doctors shown in Figure 7 indicates some evaluation error between doctors. The proposed method was accurate because the error in its results was smaller than 1, and the neighboring assessment values had very close meanings. The features chosen by feature selection were closely related to the aspects of evaluation attended to by the Kampo doctors. The most important features were the tongue gloss, color, and shape in the diagnosis of qi deficiency; color and shape for blood deficiency; color for blood stagnation; gloss for yin deficiency; and shape for fluid stagnation. This shows that the features that were considered important by the Kampo doctors had been selected by the proposed method. However, we did not use the features of form (for example, size and tooth marks). Therefore, it was not possible to determine the dominant features of fluid stagnation, which is highly influenced by form. This was also evident in the final diagnostic results. The diagnostic accuracies for the detection of qi deficiency and fluid stagnation, where shape is important, were worse than those for the other diseases. These results suggest that the addition of new features related to shape may improve the diagnostic accuracy.  
In this study, tongue images were taken from healthy subjects. However, we were able to efficiently generate tongue data that could be diagnosed as score 4 (sick) by data augmentation. The doctors who evaluated the tongue images commented that the evaluation images were realistic, but some of the tongue colors were not realistic. In the future, we would like to improve this point in cooperation with doctors.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Diseases**} \\ \hline  & & **Qi** & **Blood** & **Blood** & **Yin** & **Fluid** \\  & & **Deficiency** & **Deficiency** & **Stagnation** & **Deficiency** & **Stagnation** \\ \hline \multirow{2}{*}{Average of all doctors} & no modu. & 0.71 & 0.73 & 0.62 & 0.67 & 0.77 \\  & modu. & 0.53 & 0.50 & 0.41 & 0.43 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Root mean squared error between the correct value (using average of values assigned by each Kampo doctor) and the value estimated by support vector regression for each disease using data with no modulation (no modu.) and modulation (modu.).
The data set created in this study contains only a small number of subjects and only Japanese subjects. In the future, we would like to collect data from more races to create a meaningful dataset that can be released to the public.  
The limitations of this study are therefore:  
Some modulated data for data augmentation were not naturally observed tongue data.  
A polarization camera is required instead of a regular camera.