Visual analysis of health checkup data using multidimensional scaling

Keiko Yamamoto, Satoshi Tamura, Satoru Hayamizu, Yasutomi Kinosada

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The objective of this study is the presentation of an analytical method to support health consultants, thereby establishing an analytical method that enables them to select subjects for health guidance using health checkup data and to derive a suitable guidance policy for each subject. This paper examines an analysis method that maps a health checkup using Multi- Dimensional Scaling (MDS). MDS mapping of multivariate health checkup data for a health checkup examinee on a two-dimensional plane facilitates comprehension of a subject's health condition easily as visual information. This study focuses on the efficacy of visualization from the viewpoint of supporting health consultants. The mode of display by MDS facilitates visual confirmation that groups outside of the scope of health guidance and at high risk are shown in a contrastive position. In addition, a medium risk group was plotted into an in-between position. A plot ofmore detailed classification for all inspection items suggests by concurrence an increased risk. Results of this study indicate that its coordinates are effective both in determining a subject's health condition intuitively and in use as one index of risk formetabolic syndrome. These results are therefore considered useful for formulating health guidance plans such as priority issues.

Original languageEnglish
Pages (from-to)26-32
Number of pages7
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume16
Issue number1
DOIs
Publication statusPublished - 2012 Jan
Externally publishedYes

Keywords

  • Health checkup
  • Multidimensional scaling
  • Visual analysis

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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