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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

Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
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Academic Editors: Wentao Wu, Zhiwen Luo and Jingru Benner 
Sustainability 2021, 13(10), 5724; https://doi.org/10.3390/su13105724
Received: 24 February 2021 / Revised: 20 April 2021 / Accepted: 20 April 2021 / Published: 20 May 2021
(This article belongs to the Special Issue Smart Building: Eco-friendly Technology)
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. View Full-Text
Keywords: indoor environment; smart building; health risk assessment; cardiovascular disease; LSTM deep learning; Bayesian fitting indoor environment; smart building; health risk assessment; cardiovascular disease; LSTM deep learning; Bayesian fitting
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MDPI and ACS Style

Zhu, R.; Lv, Y.; Wang, Z.; Chen, X. Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method. Sustainability 2021, 13, 5724. https://doi.org/10.3390/su13105724

AMA Style

Zhu R, Lv Y, Wang Z, Chen X. Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method. Sustainability. 2021; 13(10):5724. https://doi.org/10.3390/su13105724

Chicago/Turabian Style

Zhu, Rui, Yang Lv, Zhimeng Wang, and 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 13, no. 10: 5724. https://doi.org/10.3390/su13105724

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