Hidden markov model for analyzing time-series health checkup data

Ryouhei Kawamoto*, Alwis Nazir, Atsuyuki Kameyama, Takashi Ichinomiya, Keiko Yamamoto, Satoshi Tamura, Mayumi Yamamoto, Satoru Hayamizu, Yasutomi Kinosada

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


In this paper, we apply a Hidden Markov Model (HMM) to analyze time-series personal health checkup data. HMM is widely used for data having continuation and extensibility such as time-series health checkup data. Therefore, using HMM as probabilistic model to model the health checkup data is considered to be suitable, and HMM can express the process of health condition changes of a person. In this paper, a HMM with six states placed in a 2×3 matrix was prepared. We collected training features including the time-series health checkup data. Each feature consists of eight inspection parameters such as BMI, SBP, and TG. The HMM was then built using the training features. In the experiments, we built five HMMs for different gender and age conditions (e.g. male 50's) using thousands of training feature vectors, respectively. Investigating the HMMs we found that the HMMs can model three health risk levels. The models can also represent health transitions or changes, indicating the possibility of estimating the risk of lifestyle-related diseases.

Original languageEnglish
Title of host publicationMEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics
PublisherIOS Press
Number of pages5
ISBN (Print)9781614992882
Publication statusPublished - 2013
Externally publishedYes
Event14th World Congress on Medical and Health Informatics, MEDINFO 2013 - Copenhagen, Denmark
Duration: 2013 Aug 202013 Aug 23

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference14th World Congress on Medical and Health Informatics, MEDINFO 2013


  • Public health informatics
  • big data
  • data mining
  • health checkup
  • hidden Markov model
  • personal health records

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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