Improving Diagnosis Estimation by Considering the Periodic Span of the Life Cycle Based on Personal Health Data

Kiichi Tago, Shoji Nishimura, Atsushi Ogihara, Qun Jin*

*この研究の対応する著者

研究成果: Article査読

抄録

With the surge in popularity of wearable devices, collection of personal health data has become quite easy. Many studies have been conducted using health data to estimate the onset and progression of illness. However, life habits may vary among individuals. By analyzing the life cycle from health-related data, conventional studies may be improved. This study proposes a new approach to improving diagnosis estimation by considering the life cycle analyzed from health-related data. The periodic span of the life cycle is estimated via autocorrelation analysis. In the range of the periodic span, dimension reduction for health data is performed by principal component analysis, and health features are extracted and used for diagnosis estimation. In our experiment, we used personal health data and pulse diagnosis data collected by a traditional Chinese medicine doctor. Using six multi-label classification methods, we verified that a combination of pulse and health features could improve the accuracy of diagnosis estimation compared with that using only pulse features.

本文言語English
論文番号100176
ジャーナルBig Data Research
23
DOI
出版ステータスPublished - 2021 2 15

ASJC Scopus subject areas

  • 管理情報システム
  • 情報システム
  • コンピュータ サイエンスの応用
  • 情報システムおよび情報管理

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