A Predictive Model for Height Tracking in an Adult Male Population in Bangladesh to Reduce Input Errors
| Autor: | Mehdi Hasan, Kenji Hisazumi, Akira Fukuda, Rafiqul Islam, Fumihiko Yokota, Ashir Ahmed |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Waist 020205 medical informatics Computer science ehealth Health Toxicology and Mutagenesis Population lcsh:Medicine 02 engineering and technology Article Body Mass Index Young Adult Statistics 0202 electrical engineering electronic engineering information engineering Range (statistics) eHealth Humans clinical growth pattern education Aged Aged 80 and over education.field_of_study Bangladesh Anthropometry Body Weight lcsh:R Public Health Environmental and Occupational Health Middle Aged Models Theoretical portable health clinic Body Height Remote healthcare 020201 artificial intelligence & image processing Female Tracking (education) remote healthcare Error detection and correction |
| Zdroj: | International Journal of Environmental Research and Public Health, Vol 17, Iss 5, p 1806 (2020) International Journal of Environmental Research and Public Health Volume 17 Issue 5 |
| ISSN: | 1660-4601 |
| Popis: | 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&ndash 49, no significant change Age group 50&ndash 64, slightly decremented pattern and Age group 65&ndash 100, a drastic height loss. The acceptable range can change over time. The system estimates the updated trend from new health records. |
| Databáze: | OpenAIRE |
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