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

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Abstract

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.

Original languageEnglish
Article number100176
JournalBig Data Research
Volume23
DOIs
Publication statusPublished - 2021 Feb 15

Keywords

  • Daily life cycle
  • Data analysis
  • Deep learning
  • Diagnosis estimation
  • Personal health data analysis

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Science Applications
  • Information Systems and Management

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