Context-aware system for cardiac condition monitoring and management: a survey

Ogbuabor, Godwin; Augusto, Juan Carlos; Moseley, Ralph; Wyk, Aléchia van

doi:10.1080/0144929x.2020.1836255

Cited by 9 publications

(4 citation statements)

References 89 publications

(108 reference statements)

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“…The system receives contextual information, analyzes it, and assigns it an activity label (Alegre et al, 2016;Hashemi and Sadeghi-Niaraki, 2016;Kirsch-Pinheiro, 2023). A context-aware system communicates with sensors and services in a smart environment and processes the context using machine-learning techniques (Ogbuabor et al, 2022). Various techniques have been presented in the literature to model context (Mahmud et al, 2007c).…”

Section: Evidence In Literaturementioning

confidence: 99%

Augmenting context with power information for green context-awareness in smart environments

Mahmud,

Hussain

2024

Front. Comput. Sci.

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The increase in the use of smart devices has led to the realization of the Internet of Everything (IoE). The heart of an IoE environment is a Context-Aware System that facilitates service discovery, delivery, and adaptation based on context classification. The context has been defined in a domain-dependent way, traditionally. The classical models of context have been focused on rich context and lack Cost of Context (CoC) that can be used for decision support. The authors present a philosophy-inspired mathematical model of context that includes confidence in activity classification of context, the actions performed, and the power information. Since a single recurring activity can lead to distinct actions performed at different times, it is better to record the actions. The power information includes the power consumed in the complete context processing and is a quality attribute of the context. Power consumption is a useful metric as CoC and is suitable for power-constrained context awareness. To demonstrate the effectiveness of the proposed work, example contexts are described, and the context model is presented mathematically in this study. The context is aggregated with power information, and actions and confidence on the classification outcome lead to the concept of situational context. The results show that the context gathered through sensor data and deduced through remote services can be made more rich with CoC parameters.

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Section: Evidence In Literaturementioning

confidence: 99%

Augmenting context with power information for green context-awareness in smart environments

Mahmud,

Hussain

2024

Front. Comput. Sci.

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“…Most of the studies on cardiac condition monitoring focus on identifying irregularities in a specific vital sign [16]. This approach might not provide enough information for effective and efficient cardiac condition monitoring.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Support for Cardiac Health Monitoring Using Federated Machine Learning

Ogbuabor

Augusto

Moseley

et al. 2021

Lecture Notes in Computer Science

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“…Proper cardiac disease forecasting and timely warnings can significantly reduce the mortality rate. The creation of tools for predicting the risk of heart attacks relies on identifying and analyzing these risk variables, which can inform individuals about their potential vulnerabilities ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

Kalita,

Ganesh,

Jayalakshmi

et al. 2023

Front. Digit. Health

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The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

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Context-aware system for cardiac condition monitoring and management: a survey

Cited by 9 publications

References 89 publications

Augmenting context with power information for green context-awareness in smart environments

Augmenting context with power information for green context-awareness in smart environments

Context-Aware Support for Cardiac Health Monitoring Using Federated Machine Learning

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

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