Visual analysis of health checkup data using multidimensional scaling

Keiko Yamamoto*, Satoshi Tamura, Satoru Hayamizu, Yasutomi Kinosada


研究成果: Article査読


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.

ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
出版ステータスPublished - 2012 1月

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能


「Visual analysis of health checkup data using multidimensional scaling」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。