# Creating a Diagnostic Assistance System for Diseases in Kampo Medicine
## 5 Conclusions  
We developed an evaluation system that considers the gloss and color components of images of the tongue taken by a polarizing camera to diagnose diseases according to the principles of Kampo medicine. The number of samples was increased from 11 original images to 275 images by modulating the color and gloss. A total of 27 gloss- and color-related features were obtained and combined with subjective evaluation by a Kampo doctor to generate the training data. The MAE of SVR was 0.44 for qi deficiency, 0.42 for blood deficiency, 0.33 for blood stagnation, 0.36 for yin deficiency, and 0.55 for fluid stagnation, when the parameters were optimized by grid searching. The evaluation errors for all diseases were less than 1, indicating reasonable evaluation accuracy. As in [PERSON] et al. [6], after each of the four Kampo doctors made a diagnosis, machine learning was performed using each evaluation as the ground truth. In this study, it was found that individual differences were generated among doctors in their evaluation of tongue images. The highest accuracy was obtained by taking the average of the evaluation values of the doctors as the ground truth. This is because errors and blur caused by one doctor's diagnosis and knowledge were reduced by taking the average. By using machine learning to reproduce the average of multiple doctors' evaluations, it is hoped that quantitative diagnosis that eliminates the subjective blurring of individual doctors will become possible. The results of feature selection were consistent with the evaluation values assigned by the Kampo doctors, indicating their correctness. As the diagnosis of fluid stagnation requires numerous features, no useful set of features was available for this condition. Adding the shape of the tongue as a feature may improve the accuracy of the diagnosis. The advantage of this study is that the equipment is simple and easy to reproduce, and the number of data can be easily collected through data augmentation. The results of this study indicate that the polarization camera can be used to diagnose the condition of individual patients. By collecting more data and improving the accuracy of the system, it can be expected to develop into a system that allows doctors to monitor the condition of patients and provide appropriate care without having to diagnose all patients. As for the issues, the later assessments may have been more accurate as the Kampo doctors became familiar with the evaluation of tongue images. Future research should include more stable subjective evaluation methods, such as multiple practice sessions for the Kampo doctors. In future work, more tongue images should be collected and re-evaluated because some of the pathological conditions were biased, as described above. In addition, by adding the features of tongue shape, the accuracy of the proposed method can be improved for the diseases with low diagnostic accuracy in the present study. Another option is to incorporate continuous learning, as implemented by [PERSON] et al. [15]. By building a system that can continuously learn from the opinions of more physicians and tongue data with new cases, we can make more objective diagnoses with better generalization performance. Conceptualization, R.K. and K.O.-O. ; formal analysis, R.K.; investigation, K.O.-O., A.S., K.H., J.A. and H.L. ; methodology, R.K., K.O.-O. and H.L. ; software, R.K.; supervision, N.T. ; validation, A.S., K.H. and J.A. ; writing--original draft, R.K.; writing--review and editing, K.O.-O., A.S., K.H., J.A. and N.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** This study was approved by the Ethical Board of Kanazawa University, School of Medicine (2018-154). All participants agree to the publication of the results obtained from the study. All results are decoded. Individual results cannot be tracked.