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

Rui Zhu, Yang Lv, Zhimeng Wang and Xi Chen
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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

Sustainability, 2021, vol. 13, issue 10, 1-19

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.

Keywords: indoor environment; smart building; health risk assessment; cardiovascular disease; LSTM deep learning; Bayesian fitting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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