An Intelligent IoT-Based Wearable Health Monitoring System

Kassem, Ahmed; Tamazin, Mohamed; Aly, Moustafa H.

doi:10.1007/978-3-030-39847-7_29

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The proposed system makes intelligent patient care, monitoring, and management decisions using ensemble machine learning and fuzzy logic to determine the possible con-Figure 18. Comparative analysis of performance parameters with the existing methods, [8,9,12,15,18,25,28,30,32,39].…”

Section: Discussionmentioning

confidence: 99%

“…A low-cost, high-quality, versatile, wearable smart device was designed to track fitness athletes and patients with heart disorders in the healthcare system [14]. An innovative and intelligent healthcare system has been created based on cutting-edge innovations like ML and the Internet of Things (IoT) [13,15]. A suggested IoT-based healthcare infrastructure would offer patients remote monitoring in an emergency [16].…”

Section: Related Workmentioning

confidence: 99%

“…Figure 18.Comparative analysis of performance parameters with the existing methods,[8,9,12,15,18,25,28,30,32,39] …”

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confidence: 99%

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A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters

2022

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The tremendous growth of health-related digital information has transformed machine learning algorithms, allowing them to deliver more relevant information while remotely monitoring patients in modern telemedicine. However, patients with epilepsy are likely to die or have post-traumatic difficulties. As a result, early disease detection could be essential for a person’s survival. Hence, early diagnosis of epilepsy based on health parameters is needed. This paper presents a classification of epilepsy disease based on wearable-sensor health parameters that use a hybrid approach with ensemble machine learning and a fuzzy logic inference system. The ensemble machine learning classifiers are used to predict epilepsy events using ensemble bagging and ensemble boosting regression. The experimental results show that compared to the ensemble bagging classifiers and other state-of-the-art methods, the ensemble boosting classifier with the fuzzy inference system outperformed with a 97% accuracy rate.

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