A Predictive Model for Height Tracking in an Adult Male Population in Bangladesh to Reduce Input Errors
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...
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| Veröffentlicht in: | International journal of environmental research and public health 2020-03, Vol.17 (5), p.1806 |
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| creator | Hasan, Mehdi Yokota, Fumihiko Islam, Rafiqul Hisazumi, Kenji Fukuda, Akira Ahmed, Ashir |
| description | 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. |
| doi_str_mv | 10.3390/ijerph17051806 |
| format | Article |
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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. 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| source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry; PubMed Central Open Access |
| subjects | Age Anthropometry Archives & records Body height Clinics Developing countries Digitization Error detection Females Gender Growth patterns Health care LDCs Males Medical equipment Medical personnel Morbidity Personal health Prediction models Remote sensors Telemedicine Upgrading |
| title | A Predictive Model for Height Tracking in an Adult Male Population in Bangladesh to Reduce Input Errors |
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