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Article

A Predictive Model for Height Tracking in an Adult Male Population in Bangladesh to Reduce Input Errors

1
Department of Advanced Information Technology, Kyushu University, Fukuoka 819-0395, Japan
2
Institute of Decision Science for a Sustainable Society, Kyushu University, Fukuoka 819-0395, Japan
3
Medical Information Center, Kyushu University Hospital, Fukuoka 812-8582, Japan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(5), 1806; https://doi.org/10.3390/ijerph17051806
Received: 15 November 2019 / Revised: 21 February 2020 / Accepted: 3 March 2020 / Published: 10 March 2020
(This article belongs to the Special Issue Digital Health Interventions in Everyday Settings)
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. View Full-Text
Keywords: clinical growth pattern; portable health clinic; remote healthcare; eHealth clinical growth pattern; portable health clinic; remote healthcare; eHealth
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MDPI and ACS Style

Hasan, M.; Yokota, F.; Islam, R.; Hisazumi, K.; Fukuda, A.; Ahmed, A. A Predictive Model for Height Tracking in an Adult Male Population in Bangladesh to Reduce Input Errors. Int. J. Environ. Res. Public Health 2020, 17, 1806. https://doi.org/10.3390/ijerph17051806

AMA Style

Hasan M, Yokota F, Islam R, Hisazumi K, Fukuda A, Ahmed A. A Predictive Model for Height Tracking in an Adult Male Population in Bangladesh to Reduce Input Errors. International Journal of Environmental Research and Public Health. 2020; 17(5):1806. https://doi.org/10.3390/ijerph17051806

Chicago/Turabian Style

Hasan, Mehdi, Fumihiko Yokota, Rafiqul Islam, Kenji Hisazumi, Akira Fukuda, and Ashir Ahmed. 2020. "A Predictive Model for Height Tracking in an Adult Male Population in Bangladesh to Reduce Input Errors" International Journal of Environmental Research and Public Health 17, no. 5: 1806. https://doi.org/10.3390/ijerph17051806

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