Classification of TCM pulse diagnoses based on pulse and periodic features from personal health data

Kiichi Tago, Haidong Wang, Qun Jin

研究成果

4 被引用数 (Scopus)

抄録

Pulse diagnosis is one of the diagnostic methods in traditional Chinese medicine (TCM). Such diagnoses are made subjectively by a TCM doctor, who requires expert knowledge. If pulse diagnosis could be automated, it would be beneficial for health management. In our previous study, we showed that pulse diagnosis might be related to personal health data, such as step count and sleep score. In this study, we propose a new approach to classifying pulse diagnoses based on a combination of features from pulse and health data. Pulse characteristics are extracted from electronically recorded pulse shapes, and health data feature analysis is augmented by considering the periodicity of daily health metrics. Using these features, we perform both single- and multi-label classifications, and investigate the possibility to improve classification accuracy. We further adopt two classification methods for multi-label classification: random forests and deep learning. Our results show that our approach improves classification accuracy for pulse diagnoses.

本文言語English
ホスト出版物のタイトル2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728109626
DOI
出版ステータスPublished - 2019 12
イベント2019 IEEE Global Communications Conference, GLOBECOM 2019 - Waikoloa, United States
継続期間: 2019 12 92019 12 13

出版物シリーズ

名前2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings

Conference

Conference2019 IEEE Global Communications Conference, GLOBECOM 2019
国/地域United States
CityWaikoloa
Period19/12/919/12/13

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ
  • 情報システム
  • 信号処理
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理
  • メディア記述
  • 健康情報学

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