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Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method

Author

Listed:
  • Rui Zhu

    (Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China)

  • Yang Lv

    (Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China)

  • Zhimeng Wang

    (Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China)

  • Xi Chen

    (Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract

Hypertension has become the greatest risk factor for death in elderly populations. As factors influencing cardiovascular disease, indoor environmental parameters pose potential risks for older adults. In this study, elderly residents in Dalian (Liaoning Province, China) urban dwellings were selected as the research subjects, and the environmental parameters of the dwellings’ main activity rooms and the blood pressure parameters of the older adults were measured. Based on the Long Short-Term Memory (LSTM) deep learning algorithm and Bayesian fitting method, a hypertension disease model was established using the long-term environmental parameters to predict the hypertension risk of older adults in their building’s environment. The results showed that temperature, humidity, and some air quality parameters had an impact on blood pressure under single environmental factor, and the comprehensive environmental risks of high systolic blood pressure, high diastolic blood pressure, and high blood pressure were 16.44%, 0%, and 16.44% for the male elderly and 14.11%, 7.14%, and 17.55% for the female elderly, respectively. By comparing the results for the blood pressure measurement and prediction, it can be observed that the risk error of hypertension obtained by the algorithm maintains the variables’ relationship, and the result of the algorithm is reliable in this period. This technology can provide a basis for measuring environmental parameters and will be conducive to the development of an ecological smart building environment.

Suggested Citation

  • Rui Zhu & Yang Lv & Zhimeng Wang & Xi Chen, 2021. "Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method," Sustainability, MDPI, vol. 13(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5724-:d:558242
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    References listed on IDEAS

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    1. Xiyao Chen & Fei Li & Chaoyang Liu & Jun Yang & Jingdong Zhang & Chunlin Peng, 2017. "Monitoring, Human Health Risk Assessment and Optimized Management for Typical Pollutants in Indoor Air from Random Families of University Staff, Wuhan City, China," Sustainability, MDPI, vol. 9(7), pages 1-13, June.
    2. Jesica Fernández-Agüera & Samuel Dominguez-Amarillo & Marco Fornaciari & Fabio Orlandi, 2019. "TVOCs and PM 2.5 in Naturally Ventilated Homes: Three Case Studies in a Mild Climate," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    3. Chen, Xu & Xu, Bin & Mei, Congli & Ding, Yuhan & Li, Kangji, 2018. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation," Applied Energy, Elsevier, vol. 212(C), pages 1578-1588.
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    Cited by:

    1. Hyerim Kim & Seunghyeon Hwang & Suwon Lee & Yoona Kim, 2022. "Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm," IJERPH, MDPI, vol. 19(22), pages 1-20, November.
    2. Jiping Xu & Ziyi Wang & Xin Zhang & Jiabin Yu & Xiaoyu Cui & Yan Zhou & Zhiyao Zhao, 2022. "A Rice Security Risk Assessment Method Based on the Fusion of Multiple Machine Learning Models," Agriculture, MDPI, vol. 12(6), pages 1-15, June.

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