Advancing hospital healthcare: achieving IoT-based secure health monitoring through multilayer machine learning DOI Creative Commons
Ke Qi

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 3, 2025

Abstract Background Data based clinical decision support system is a boon for health care monitoring. Smart healthcare monitoring systems play vital role in the early diagnosis and detection of physical mental patients. The smart IoT (C-IoT) are data-driven provide efficient this purpose. Purpose There need to have secure, accurate, HCM that capable processing large amounts patient data timely various complications. Traditional ways migration imprecise, less do not cover all angles necessary contemporary environment. Because this, conceptual IoT-based secure employs machine learning algorithms enhanced accuracy. Method This study presents conjugate applications with cloud-based C-IoT model systems. lightweight encryption block maintains provisional security data. It assists patient’s issues which diagnosed existing database history proper measures taken using prediction model. status from pre-historical database. Results shows results approximately 91% accuracy while Artificial Neural Network (ANN) algorithms. C-IoT-based issue diagnostic one step ahead toward modernization society 5.0. Future prospects proposed expands surgeries by achieving high employing ANN algorithms, excellence founded on intensity prior data, Aligned Society 5.0, it brings new, friendly, features replace many methods better ones terms precision, security, coverage.

Language: Английский

Advancing hospital healthcare: achieving IoT-based secure health monitoring through multilayer machine learning DOI Creative Commons
Ke Qi

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 3, 2025

Abstract Background Data based clinical decision support system is a boon for health care monitoring. Smart healthcare monitoring systems play vital role in the early diagnosis and detection of physical mental patients. The smart IoT (C-IoT) are data-driven provide efficient this purpose. Purpose There need to have secure, accurate, HCM that capable processing large amounts patient data timely various complications. Traditional ways migration imprecise, less do not cover all angles necessary contemporary environment. Because this, conceptual IoT-based secure employs machine learning algorithms enhanced accuracy. Method This study presents conjugate applications with cloud-based C-IoT model systems. lightweight encryption block maintains provisional security data. It assists patient’s issues which diagnosed existing database history proper measures taken using prediction model. status from pre-historical database. Results shows results approximately 91% accuracy while Artificial Neural Network (ANN) algorithms. C-IoT-based issue diagnostic one step ahead toward modernization society 5.0. Future prospects proposed expands surgeries by achieving high employing ANN algorithms, excellence founded on intensity prior data, Aligned Society 5.0, it brings new, friendly, features replace many methods better ones terms precision, security, coverage.

Language: Английский

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