Paper Info
Title
Improving Diagnosis Estimation By Considering The Periodic Span Of The Life Cycle Based On Personal Health Data
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. (C) 2020 Elsevier Inc. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.bdr.2020.100176
BIG DATA RESEARCH
Keywords
DocType
Volume
Diagnosis estimation, Daily life cycle, Deep learning, Personal health data analysis, Data analysis
Journal
23
ISSN
Citations 
PageRank 
2214-5796
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Kiichi Tago122.42
Shoji Nishimura219116.40
Atsushi Ogihara300.34
Qun Jin400.34