A predictive model for height tracking in an adult male population in bangladesh to reduce input errors

Mehdi Hasan, Ashir Ahmed, Fumihiko Yokota, Rafiqul Islam, Kenji Hisazumi, Akira Fukuda

研究成果: Contribution to journalArticle査読

抄録

The advancement of ICT and affordability of medical sensors enable healthcare data to be obtained remotely. Remote healthcare data is erroneous in nature. Detection of errors for remote healthcare data has not been significantly studied. This research aims to design and develop a software system to detect and reduce such healthcare data errors. Enormous research efforts produced error detection algorithms, however, the detection is done at the server side after a substantial amount of data is archived. Errors can be efficiently reduced if the suspicious data can be detected at the source. We took the approach to predict acceptable range of anthropometric data of each patient. We analyzed 40,391 records to monitor the growth patterns. We plotted the anthropometric items e.g., Height, Weight, BMI, Waist and Hip size for males and females. The plots show some patterns based on different age groups. This paper reports one parameter, height of males. We found three groups that can be classified with similar growth patterns: Age group 20–49, no significant change; Age group 50–64, slightly decremented pattern; and Age group 65–100, a drastic height loss. The acceptable range can change over time. The system estimates the updated trend from new health records.

本文言語英語
論文番号1806
ジャーナルInternational journal of environmental research and public health
17
5
DOI
出版ステータス出版済み - 3 1 2020

All Science Journal Classification (ASJC) codes

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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