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Prediction of hypertension risk in the elderly under structured conditions based on LSTM deep learning and Bayesian fitting method
Received: 24 February 2021 / Revised: 20 April 2021 / Accepted: 20 April 2021 / Published: 20 May 2021
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Hypertension has become the largest risk factor for mortality in the elderly population. Indoor environmental parameters as risk factors for cardiovascular disease in older adults. In this study, elderly residents in Dalian (Lianing Province, China) urban residences were selected as research subjects, and the environmental parameters of the main working rooms of the residences and the blood pressure parameters of the elderly were measured. Based on a long-term memory (LSTM) deep learning algorithm and a Bayesian fitting method, a hypertension model with long-term environmental parameters was developed to predict the risk of hypertension in the elderly under construction conditions. The results show that some parameters of temperature, humidity and air quality influence blood pressure under one environmental factor and the environmental risk of high systolic blood pressure, high diastolic blood pressure and high blood pressure are 16.44%, 0% and 16.44 for Male age. %, 14.11%, 7.14% and 17.55% for female age respectively. By comparing the results for blood pressure measurement and prediction, it can be observed that the hypertension risk error obtained by the algorithm maintains the relationship of the variables 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 ecologically smart building environment.
Closed environment; smart building; health risk assessment; cardiovascular disease; LSTM deep learning; Bayesian closed environment; smart building; health risk assessment; cardiovascular disease; LSTM deep learning; Bayesian fit
With the growth of urbanization, developed countries have experienced demographic changes in their populations, and the percentage of older adults has increased. It is estimated that in 2035, the number of elderly people and the aging rate will be 392 million and 30.5%, respectively, making the world an aging society [1]. According to data released by China’s National Bureau of Statistics, at the end of 2019, the population over 60 years old accounted for 18.1% of the total population, and the population over 65 years old accounted for 12.6% [2]. The American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) states that the indoor environment affects human health and that the environment is related to physical health conditions [3]. Several papers have suggested that people may be exposed to related diseases and building syndromes due to indoor or outdoor pollution [4, 5]. Compared to other people, the elderly spend more time in indoor activities, so it is of great importance to study the relationship between the health of the elderly and the indoor environment [6]. In 2015, it was found that almost half of the world’s elderly died from cardiovascular diseases [7]. Compared to other seasons, the prevalence of cardiovascular disease is highest in winter [8]. Studies have shown that temperature differences will affect the incidence and mortality of cardiovascular diseases, which are associated with air pollution [11, 12]. As one of the main determinants of cardiovascular disease, blood pressure has a high predictive value for the elderly [13]; Monitoring of systolic blood pressure is more important than diastolic blood pressure [14]. Controlling systolic blood pressure can reduce the risk of cerebrovascular disease and stroke; Thus, it is a major target for improving the prognosis of elderly patients [15]. It makes no sense to control indoor parameters just to meet building environmental standards. On the one hand, standards are based on the needs of young people; On the other hand, individual body differences cannot be ignored [16]. The three main concepts of the “Healthy China” strategy – “Higher Health”, “All-round” and “Higher Environment” – put forward a new idea to solve the most pressing issues in the field of environment and health. growth Objectives for the care of intelligent and healthy elderly [17].
Some scholars have proposed Ambient Assisted Living (AAL) technology earlier, and some studies have shown that the application of deep learning model to AAL service can significantly improve the accuracy and reliability of life records [18, 19]. A deep learning algorithm acquires data from Internet of Things (IoT) device sensors in a deployment environment. This algorithm uses advanced techniques to significantly improve the performance of existing machine learning technologies and is rapidly developing in the field of biological data and health data [20, 21]. Deep learning algorithms can extract feature information from human physiological parameters for pattern recognition and health assessment [22, 23]. Some scientists have systematically studied the application of deep learning algorithms in automatic diagnosis of diabetes. This model used neural networks based on data augmentation and data correction technology, which includes various physiological parameters of the human body and proves its ability to predict diseases [24, 25, 26]. The following methods are used to measure and estimate blood pressure: Blood pressure can be recovered by invasive ambulatory blood pressure assessment based on photoplethysmography (PPG) by combining the classical pulse width estimation model and the nervous system model to estimate blood pressure. signals, blood pressure training and prediction can be performed by LSTM [28]. Convolutional deep learning neural network (CNN) and LSTM-based methods can be used to predict time series data in electronic health records [29]. A study in the field of clinical cardiology showed that deep learning algorithms were superior to clinicians in predicting prognosis and future events in patients with pulmonary hypertension [30]. With the development of IoT technology, IoT will gradually become the basis of home appliances [31]. In summary, many artificial intelligence diagnostic algorithms have been developed for disease detection, but the prediction accuracy for hypertension is lower. In addition, predicting blood pressure parameters ignores the interference of external factors, and indoor environment IoT products ignore the dynamic changes of human health.
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Currently, indoor air quality research is mainly conducted in two areas: assessment and management [32]. Older adults are interested in using visualization technology to monitor long-term health trends [33]. In this study, we tested morning and evening environmental measurements and blood pressure parameters for urban residential buildings in Dalian, and prepared to build a future Internet of Things data platform and mobile application. The research objectives of this paper are: (1) to monitor indoor temperature, humidity, formaldehyde, carbon dioxide, volatile organic compounds (TVOC) and PM;
Use the standard for environmental assessment; (2) to analyze the effect of an environmental factor on aging blood pressure; .
In the first phase, we conducted a survey using a questionnaire. The content of the questionnaire is about the living environment, the type of thermal insulation of the envelope, the type of floor coverings, the personal thermal comfort of the residents, living and eating habits, etc. included questions We sent 50 questionnaires within Dalian City (Liaoning Province, China) Because understanding the habits and living conditions of elderly residents requires good network conditions of IoT devices, this study is a
