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

Zhu, Rui; Lv, Yang; Wang, Zhimeng; Chen, Xi

doi:10.3390/su13105724

Cited by 4 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We attempted to include as many variables as possible related to hypertension risk factors [ 40 , 41 , 42 , 43 ]. Finally, 61 independent variables were included, such as: general characteristics [n = 33; e.g., sex, age, body mass index (BMI), waist circumference, education, income, smoking and drinking], nutrient intakes (n = 23), and dietary patterns (n = 5).…”

Section: Methodsmentioning

confidence: 99%

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm

Kim

Hwang

Lee

et al. 2022

IJERPH

View full text Add to dashboard Cite

Few studies classified and predicted hypertension using blood pressure (BP)-related determinants in a deep learning algorithm. The objective of this study is to develop a deep learning algorithm for the classification and prediction of hypertension with BP-related factors based on the Korean Genome and Epidemiology Study-Ansan and Ansung baseline survey. We also investigated whether energy intake adjustment is adequate for deep learning algorithms. We constructed a deep neural network (DNN) in which the number of hidden layers and the number of nodes in each hidden layer are experimentally selected, and we trained the DNN to diagnose hypertension using the dataset while varying the energy intake adjustment method in four ways. For comparison, we trained a decision tree in the same way. Experimental results showed that the DNN performs better than the decision tree in all aspects, such as having higher sensitivity, specificity, F1-score, and accuracy. In addition, we found that unlike general machine learning algorithms, including the decision tree, the DNNs perform best when energy intake is not adjusted. The result indicates that energy intake adjustment is not required when using a deep learning algorithm to classify and predict hypertension with BP-related factors.

show abstract

Section: Methodsmentioning

confidence: 99%

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm

Kim

Hwang

Lee

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…The Long short-term Memory (LSTM) [15] is a Recurrent Neural Network (RNN) that incorporates memory capacity [16], representing an advancement over traditional RNNs. When dealing with long-term dependent data, the generalization capabilities of RNNs are suboptimal.…”

Section: B Bidirectional Long Short Memory Networkmentioning

confidence: 99%

Recongnition of Distracted Driving Behavior Based on Improved Bi-LSTM Model and Attention Mechanism

Wang,

Yao

2024

IEEE Access

View full text Add to dashboard Cite

Distracted driving, a leading cause of traffic accidents with severe consequences, still faces numerous technical challenges in practical implementation for recognizing unsafe driving behavior. These challenges include the complexity of feature extraction using traditional convolutional neural networks (CNNs) for driver behavior analysis and the lack of real-time perception during driving. To address these issues, this study proposes an improved method for distracted driving behavior recognition by combining the Bi-LSTM model with an attention mechanism based on Dilated Convolutional Neural Networks (ID-CNN). Firstly, we employ a dilated convolution model to extract features efficiently with fewer parameters while enhancing multi-scale feature extraction capabilities and widening the receptive field. Subsequently, we integrate the attention mechanism into the Bi-LSTM model to enhance its effectiveness in solving the driving behavior classification problem. The integrated Bi-LSTM model with attention mechanism calculates correlation between intermediate and final states to obtain a probability distribution of attention weights at each moment, thereby reducing information redundancy while preserving useful information effectively. Furthermore, image feature vectors are enhanced to further improve accuracy in image classification tasks. Compared to other methods, the proposed approach exhibits faster convergence rates and more stable model accuracy. Specifically, on both the StateFarm dataset and our own collected Drive&Act-Distracted data, we achieved accuracies of 95.8367% and 97.8911%, respectively. This indicates that incorporating dilated convolution and attention mechanisms strengthens sequence data learning and feature weighting within our network model, resulting in significantly improved accuracy for driving behavior recognition.

show abstract

“…x i,j = rand × UB j − LB j − LB j (14) Where is a random number, are a problem, respectively. Each iteration ends with an approximation of the optimal solution based on the best answer acquired so far.…”

Section: Population Initializationmentioning

confidence: 99%

“…One of the most often utilised criteria was the basic ADL (BADL) [13], which assesses fundamental capabilities including getting dressed, eating, and using the bathroom on one's own. The instrumental ADL (IADL) scale, developed by Lawton et al [14] to evaluate ADL in the elderly, is a measure of how well an individual can adapt to their immediate surroundings in order to perform tasks like talking on the phone, going grocery shopping, preparing meals, and cleaning up after themselves. Given the strong association between BADL/IADL and functional ability, it was crucial to first determine the unique BADL/IADL profile in order to categorise the vast range of ageing populations in terms of their functional abilities [15].…”

Section: Introductionmentioning

confidence: 99%

Daily Activities of Elder Adults Using Optimized Deep Learning Model in China

Yang,

Rosni,

Binti Zainol

et al. 2023

Int. j. commun. netw. inf. secur.

View full text Add to dashboard Cite

The health and happiness of seniors can be influenced by the design of public spaces and buildings. Planning for age-friendly communities requires taking into account the wide variety of daily activities and public facility consumption among older persons. Yet, conventional approaches fall short when it comes to providing comprehensive, objective measures of the actions of the elderly. This research proposes an attention-based recognition approach by decomposing neural network outputs into class-dependent features using dictionary learning to improve discrimination performance. This research aimed to develop an empirical typology of activity of daily living (ADL) and its connection to health in China's ageing population. A deep learning-based model was rummaged to categorise the contributors in the Chinese Longitudinal Healthy Longevity Study (CLHLS) into subgroups based on their abilities with ADL. To be more precise, the study trains a category-based recognition network (CRN) by superimposing a basic but effective specific recognition encoding (SRE) unit on top of convolutional layers. In order to encode attention maps for each class, the SRE module uses the feature maps produced by the Convolutional Neural Networks (CNN) to learn a class-specific vocabulary. Class-wise adaptive feature refining is achieved by multiplying these attention maps by the input features. In this case, a gazelle optimization algorithm (GOA) handles the hyper-parameter tuning process of the proposed model. This case study is the first of its kind in a dense metropolitan region, and it uses objective measurements to evaluate the activity spaces of the elderly. This research provides empirical data for the promotion of healthy ageing in urban areas.

show abstract

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

Cited by 4 publications

References 47 publications

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm

Recongnition of Distracted Driving Behavior Based on Improved Bi-LSTM Model and Attention Mechanism

Daily Activities of Elder Adults Using Optimized Deep Learning Model in China

Contact Info

Product

Resources

About