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Enhanced heart disease prediction in remote healthcare monitoring using IoT-enabled cloud-based XGBoost and Bi-LSTM

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Název: Enhanced heart disease prediction in remote healthcare monitoring using IoT-enabled cloud-based XGBoost and Bi-LSTM
Autoři: Sarah A. Alzakari, Amir Abdel Menaem, Nadir Omer, Amr Abozeid, Loay F. Hussein, Islam Abdalla Mohamed Abass, Ayadi Rami, Ahmed Elhadad
Zdroj: Alexandria Engineering Journal, Vol 105, Iss, Pp 280-291 (2024)
Informace o vydavateli: Elsevier BV, 2024.
Rok vydání: 2024
Témata: IoT remote healthcare, 0202 electrical engineering, electronic engineering, information engineering, Proactive health monitoring, Extreme gradient boosting, 02 engineering and technology, Heart disease, TA1-2040, Engineering (General). Civil engineering (General), Bidirectional long short-term memory, Remote healthcare monitoring, 3. Good health
Popis: The advancement of medical technology has brought about a significant transformation in remote healthcare monitoring, which is crucial for providing customized care and ongoing observation. This is especially important when it comes to controlling long-term illnesses like high blood pressure, which raises the risk of heart disease considerably, especially in older people. This methodology achieves greater accuracy by combining regular medical monitoring and Electronic Clinical Data (ECD) from complete medical records with physical data from patients' routine medical monitoring. This innovative technique enhances the area of cardiac disease prediction. A technique that uses cutting-edge machine learning models and IoT technology to meet this demand. In particular, we use the powerful Extreme Gradient Boosting (XGBoost) algorithm to effectively examine big datasets and extract important characteristics to improve prediction accuracy. The deep learning model Bidirectional Long Short-Term Memory (Bi-LSTM) is used to further enhance prediction skills to extract complex temporal patterns from patient data. It outperformed naive Bayes, decision trees, and random forests with our approach, achieving a greater prediction accuracy of 99.4 %. With the combination of Internet of Things technologies and sophisticated machine learning models, this paper offers a novel approach to remote healthcare monitoring.
Druh dokumentu: Article
Jazyk: English
ISSN: 1110-0168
DOI: 10.1016/j.aej.2024.06.036
Přístupová URL adresa: https://doaj.org/article/1c79ff59193e47c1955f9bc3a8db00df
Rights: CC BY NC ND
Přístupové číslo: edsair.doi.dedup.....12d1e9e573c92d37a9a3c6602604657b
Databáze: OpenAIRE
Popis
Abstrakt:The advancement of medical technology has brought about a significant transformation in remote healthcare monitoring, which is crucial for providing customized care and ongoing observation. This is especially important when it comes to controlling long-term illnesses like high blood pressure, which raises the risk of heart disease considerably, especially in older people. This methodology achieves greater accuracy by combining regular medical monitoring and Electronic Clinical Data (ECD) from complete medical records with physical data from patients' routine medical monitoring. This innovative technique enhances the area of cardiac disease prediction. A technique that uses cutting-edge machine learning models and IoT technology to meet this demand. In particular, we use the powerful Extreme Gradient Boosting (XGBoost) algorithm to effectively examine big datasets and extract important characteristics to improve prediction accuracy. The deep learning model Bidirectional Long Short-Term Memory (Bi-LSTM) is used to further enhance prediction skills to extract complex temporal patterns from patient data. It outperformed naive Bayes, decision trees, and random forests with our approach, achieving a greater prediction accuracy of 99.4 %. With the combination of Internet of Things technologies and sophisticated machine learning models, this paper offers a novel approach to remote healthcare monitoring.
ISSN:11100168
DOI:10.1016/j.aej.2024.06.036