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Binary-Relevance Classification of Depression and Anxiety in the Elderly Using Low-Cost Activity Trackers

Title
Binary-Relevance Classification of Depression and Anxiety in the Elderly Using Low-Cost Activity Trackers
Authors
Sim, Jae-KyeongKim, Geon HaChoi, Mun-Taek
Ewha Authors
김건하
Issue Date
2020
Journal Title
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
ISSN
2156-7018JCR Link

2156-7026JCR Link
Citation
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS vol. 10, no. 6, pp. 1423 - 1428
Keywords
Multi-Label ClassificationBinary RelevanceDepressionAnxietyActivity TrackersCircadian Rhythms
Publisher
AMER SCIENTIFIC PUBLISHERS
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
Understanding mood disorders of depression and anxiety in the elderly is important because those are common symptoms of dementia. This paper shows our methodology of supervised learning to understand the relationship between the diagnosis of depression and/or anxiety of the elderly and their activity patterns. Using low-cost activity trackers, activity data of the elderly were collected over several weeks per subject. We extracted features from the time-series activity data based on circadian rhythms. We have applied binary relevance to this problem of multi-label nature diagnosed with depression, anxiety or both. We tried various classification algorithms for each label and compared the performances to find the best classifier among the algorithms. The result yielded that the best classifier was the F-1 score of 77.4% in the classification. Our study has yielded a meaningful result in screening mood disorders of the elderly using low-cost activity trackers.
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DOI
10.1166/jmihi.2020.3065
Appears in Collections:
의료원 > 의료원 > Journal papers
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