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JSAI International Symposium on Artificial Intelligence

JSAI-isAI 2018: New Frontiers in Artificial Intelligence pp 74–80Cite as

Analysis of Pulse Diagnosis Data from a TCM Doctor and a Device by Random Forest

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11717)

Abstract

Pulse diagnosis is a typical diagnosis of Traditional Chinese Medicine (TCM). However, it is not clear if there is any relationship between the result of pulse diagnosis and other health related data. In this study, we investigate this and analyze pulse diagnosis data from a TCM doctor and a pulse diagnostic instrument (PDI) by Random Forest. Subjects’ vital signs and pulse diagnosis data from a TCM doctor are used as training data. We classify vital signs which have the PDI’s diagnoses labels. As a result, classification accuracies were over 60% in all cases. Our experiment results imply that better pulse diagnosis may be made with assistance of personal health data analysis.

Keywords

  • Health data analysis
  • Pulse diagnosis
  • Random Forest

This work is partly supported by 2016–2018 Masaru Ibuka Foundation Research Project on Oriental Medicine.

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    37 degree bracelet, 37 Degree Technology, http://www.37c.cc/en/index.html.

  2. 2.

    DS01-C Information Collection System of Pulse Condition Diagnosis (Shanghai FDA Food and Drug Administration No. 20152270429), Daosh Medical Technology Co., Ltd., http://www.daosh.com/en/.

References

  1. Grif, M., Ayush, Y.: Data analysis of expert systems by pulse diagnosis. In: Proceedings of the 11th International Forum on Strategic Technology (IFOST), Novosibirsk, Russia, pp. 329–332. IEEE (2016)

    Google Scholar 

  2. Duraisamy, R., Dinakar, S., Venkittaramanujam, V., Jeyakumar, V.: A systematic approach for pulse diagnosis based on siddha medical procedures. In: Proceedings of the 4th International Conference on Signal Processing, Communications and Netwotrking (ICSCN), Chennai, India, pp. 1–6. IEEE (2017)

    Google Scholar 

  3. Gong, S., Xu, B., Sun, G., Chen, M., Wang, N., Dong, C., Wang, P., Cui, J.: Accurate Cirrhosis identification with wrist-pulse data for mobile healthcare. In: Proceedings of the 2nd ACM Workshop on Mobile Systems, Applications, and Services for HealthCare, Toronto, Canada, pp. 1–6. ACM (2012)

    Google Scholar 

  4. Yang, C.L., Chang, T.C., Chen, Y.Y.: Microwave sensors applying for traditional Chinese medicine pulse diagnosis. In: Proceedings of the 8th International Workshop on Electromagnetics: Applications and Student Innovation Competition, London, UK, pp. 113–115. IEEE (2017)

    Google Scholar 

  5. Peng, J.Y., Lu, M.S.-C.: A flexible capacitive tactile sensor array with CMOS readout circuits for pulse diagnosis. IEEE Sens. J. 15(2), 1170–1177 (2015)

    CrossRef  MathSciNet  Google Scholar 

  6. McLellan, S., et al.: A microprocessor-based wrist pulse simulator for pulse diagnosis in traditional Chinese medicine. In: 40th IEEE Annual Northeast Bioengineering Conference (NEBEC), Boston, USA, pp. 1–2. IEEE (2014)

    Google Scholar 

  7. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    CrossRef  Google Scholar 

  8. Saiprasad, G., Chang, C.I., Safdar, N., Saenz, N., Siegel, E.: Adrenal gland abnormality detection using random forest classification. J. Digit. Imaging 26(5), 891–897 (2013)

    CrossRef  Google Scholar 

  9. Machado, G., Mendoza, M.R., Corbellini, L.G.: What variables are important in predicting bovine viral diarrhea virus? A random forest approach. Vet. Res. 46(85), 1–15 (2015)

    Google Scholar 

  10. Boucekine, M.: Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study. BMC Med. Res. Methodol. 13(20), 1–8 (2013)

    Google Scholar 

  11. Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-09823-4_34

    CrossRef  Google Scholar 

  12. Ward, J.R.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)

    CrossRef  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Graduate School of Human Sciences, Waseda University, Tokorozawa, Japan

    Kiichi Tago

  2. Faculty of Human Sciences, Waseda University, Tokorozawa, Japan

    Atsushi Ogihara, Shoji Nishimura & Qun Jin

Authors
  1. Kiichi Tago
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  2. Atsushi Ogihara
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  3. Shoji Nishimura
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  4. Qun Jin
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Corresponding author

Correspondence to Kiichi Tago .

Editor information

Editors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan

    Kazuhiro Kojima

  2. University of Electro-Communications, Tokyo, Japan

    Maki Sakamoto

  3. Ochanomizu University, Tokyo, Japan

    Koji Mineshima

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

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Tago, K., Ogihara, A., Nishimura, S., Jin, Q. (2019). Analysis of Pulse Diagnosis Data from a TCM Doctor and a Device by Random Forest. In: Kojima, K., Sakamoto, M., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2018. Lecture Notes in Computer Science(), vol 11717. Springer, Cham. https://doi.org/10.1007/978-3-030-31605-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-31605-1_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-31605-1

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