Modified Bald Eagle Search Algorithm with Deep Learning-Driven Sleep Quality Prediction for Healthcare Monitoring Systems

Sleep habits are strongly related to health behaviors, with sleep quality serving as a major health indicator. Current approaches for evaluating sleep quality, namely polysomnography and questionnaires, are often time-consuming, costly, or invasive. Thus, there is a pressing need for a more convenie...

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Vydáno v:IEEE access Ročník 11; s. 1
Hlavní autoři: Alabdan, Rana, Mengash, Hanan Abdullah, Maray, Mohammed, Alotaibi, Faiz, Abdelbagi, Sitelbanat, Mahmud, Ahmed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial intelligence
Deep learning
Health care
Healthcare monitoring
Machine learning
Medical services
Monitoring
Monitoring systems
Parameter tuning
Prediction algorithms
Prediction models
Predictive models
Search algorithms
Sleep
Sleep apnea
Sleep quality prediction
Tuning
ISSN:2169-3536, 2169-3536
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Shrnutí:Sleep habits are strongly related to health behaviors, with sleep quality serving as a major health indicator. Current approaches for evaluating sleep quality, namely polysomnography and questionnaires, are often time-consuming, costly, or invasive. Thus, there is a pressing need for a more convenient, nonintrusive, and cost-effective method. The applications of deep learning (DL) in sleep quality prediction represent a groundbreaking technique for addressing sleep-related disorders. In this aspect, the article offers the design of a Modified Bald Eagle Search Algorithm with Deep Learning-Driven Sleep Quality Prediction (MBES-DLSQP) for Healthcare Monitoring Systems. The MBES-DLSQP technique combines the strengths of a DL model with a hyperparameter tuning strategy to provide precise sleep quality predictions. At the primary stage, the MBES-DLSQP technique undergoes data pre-processing. Besides, the MBES-DLSQP technique uses a stacked sparse autoencoder (SSAE)-based prediction model, which can extract and encode high-dimensional sleep data. The MBES-DLSQP incorporates MBESA-based hyperparameter tuning which assures its optimal configurations to further boost the efficiency of the SSAE model. The experimental outcome of the MBES-DLSQP algorithm is tested on the sleep dataset from the Kaggle repository. The experimental value infers that the MBES-DLSQP technique shows promising performance in sleep quality prediction with a maximum accuracy of 98.33%.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3337647