A Supervised Learning Based Decision Support System for Multi-Sensor Healthcare Data from Wireless Body Sensor Networks

Jijesh, J J; Shankar, Shiva; Keshavamurthy, Keshavamurthy

doi:10.1007/s11277-020-07762-9

Cited by 18 publications

(21 citation statements)

References 22 publications

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“…The prevalence of chronic diseases has made a renewed request for allowing serious healthcare facilities to the persons tracking [10]. The current investigations reveal the flaws in the traditional healthcare system, implying that hospitals and clinics alone will not be able to deal with the crisis.…”

Section: Introductionmentioning

confidence: 90%

Secure and Reliable ML-based Disease Detection for a Medical Wireless Body Sensor Networks

Mohamed

Meddeb-Makhlouf

Fakhfakh

et al. 2022

International Journal of Biology and Biomedical Engineering

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The recent development of the Internet of Things (IoT) has enabled a significant technology that aids quick healthcare solutions through the use of smart wearables sensors. Indeed, undesirable events and network threats can appear in any physiological recording in Wireless Body Sensor Networks (WBSN), leading to a misdiagnosis. These events and threats are recognizable by experienced medical staff, thereby it is necessary to identify them before making any diagnosis. In this paper, a secure and energy efficient approach is proposed. For disease detection, our research provide insight into several physiological signals, including the ElectroCardioGram (ECG), ElectroMyoGram (EMG), and Blood Pressure (BP), where the security is achieved by the application of the Advanced Encryption Symmetric (AES) and the Secure Hash Algorithm (SHA). Similarly, to obtain a reasonable range of reliability, a classification procedure based on supervised Machine Learning (ML) techniques is used. The simulation results proved the accuracy and sensitivity of the system by 97% and 92%, respectively by enhancing a high level of security. Moreover, a suitable prototype is developed for medical staff to ensure the applicability of our proposal.

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Section: Introductionmentioning

confidence: 90%

Secure and Reliable ML-based Disease Detection for a Medical Wireless Body Sensor Networks

Mohamed

Meddeb-Makhlouf

Fakhfakh

et al. 2022

International Journal of Biology and Biomedical Engineering

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“…Ganguly et al [ 21 ] have also used machine learning power to find patterns in medical data fluctuations to predict CVD in the Arduino-based IoT infrastructure. A decision support system for analyzing multi-sensor healthcare data[ 22 ] is proposed to predict heart disease by the WBSN. This is done with a supervised learning approach by a modified deep belief network in conjunction with the squirrel search algorithm as a feature selection method.…”

Section: System Development Preliminariesmentioning

confidence: 99%

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

Dami¹

2022

WJCC

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The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).

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“…The suggested technique beats previous models in terms of classification accuracy and computing time, according to the experiments. It has also been demonstrated that it reduces dimensionality to a greater extent [14]. Yanjiao et al [11] proposed an ISSA.…”

Section: Related Workmentioning

confidence: 99%

“…Jijesh et al[14] created a supervised learning-based decision support system for multisensory healthcare data from wireless body sensor networks. The SSA is used to select features.…”

mentioning

confidence: 99%

Ischemic Heart Disease Prediction Using Optimized Squirrel Search Feature Selection Algorithm

2022

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In recent years, the volume in globally recognized medical data sets are increasing both with attributes and number of records. Machine learning algorithms aiming to detect and diagnose ischemic heart diseases requires high efficacy and judgment. The state of art Ischemic heart disease data sets presents several issues, including feature selection, sample size, sample imbalance, and lack of magnitude for some characteristics etc. The proposed study is primarily concerned with improving feature selection and reducing the number of features yet giving better decisions. In this study, to pick salient aspects of heart illness, an improved squirrel search optimization algorithm with a meta-heuristic approach is proposed. Comparison study of the proposed ischemic heart disease squirrel search optimization (IHDSSO) model in conjunction with random forest classifier ensures better feature selection and accuracy over 98% with respect to other state-of-the-art optimization algorithms.

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A Supervised Learning Based Decision Support System for Multi-Sensor Healthcare Data from Wireless Body Sensor Networks

Cited by 18 publications

References 22 publications

Secure and Reliable ML-based Disease Detection for a Medical Wireless Body Sensor Networks

Secure and Reliable ML-based Disease Detection for a Medical Wireless Body Sensor Networks

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

Ischemic Heart Disease Prediction Using Optimized Squirrel Search Feature Selection Algorithm

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