Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring

Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring

A multi-agent data-analytics-based approach to ubiquitous healthcare monitoring is presented in this paper. The proposed architecture gathers a patient’s vital data using wireless body area networks, and the transmitted information is separated into binary component parts and divided into...

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Main Authors: Reda Chefira, Said Rakrak
Format: Article
Language:English
Published: MDPI AG 2019-11-01
Series:Applied Sciences
Subjects:
support vector machine
classification techniques
machine learning
blood pressure
internet of things
Online Access:https://www.mdpi.com/2076-3417/9/22/4802
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spelling doaj-67db4d3b4a344649be2b13b646ca2a502020-11-25T01:55:55ZengMDPI AGApplied Sciences2076-34172019-11-01922480210.3390/app9224802app9224802Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly MonitoringReda Chefira0Said Rakrak1Applied Mathematics and Computer Science Laboratory at Faculty of Sciences and Techniques, Cadi Ayyad University, 4000 Marrakesh, MoroccoApplied Mathematics and Computer Science Laboratory at Faculty of Sciences and Techniques, Cadi Ayyad University, 4000 Marrakesh, MoroccoA multi-agent data-analytics-based approach to ubiquitous healthcare monitoring is presented in this paper. The proposed architecture gathers a patient’s vital data using wireless body area networks, and the transmitted information is separated into binary component parts and divided into related dataset categories using several classification techniques. A probabilistic procedure is then used that applies a normal (Gaussian) distribution to the analysis of new medical entries in order to assess the gravity of the anomalies detected. Finally, a data examination is carried out to gain insight. The results of the model and simulation show that the proposed architecture is highly efficient in applying smart technologies to a healthcare system, as an example of a research direction involving the Internet of Things, and offers a data platform that can be used for both medical decision making and the patient’s wellbeing and satisfaction with their medical treatment.https://www.mdpi.com/2076-3417/9/22/4802support vector machineclassification techniquesmachine learningblood pressureinternet of things
collection DOAJ
language English
format Article
sources DOAJ
author Reda Chefira
Said Rakrak
spellingShingle Reda Chefira
Said Rakrak
Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring
Applied Sciences
support vector machine
classification techniques
machine learning
blood pressure
internet of things
author_facet Reda Chefira
Said Rakrak
author_sort Reda Chefira
title Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring
title_short Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring
title_full Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring
title_fullStr Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring
title_full_unstemmed Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring
title_sort enhancing decision making through combined classification techniques and probabilistic data analysis for ubiquitous healthcare anomaly monitoring
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2019-11-01
description A multi-agent data-analytics-based approach to ubiquitous healthcare monitoring is presented in this paper. The proposed architecture gathers a patient’s vital data using wireless body area networks, and the transmitted information is separated into binary component parts and divided into related dataset categories using several classification techniques. A probabilistic procedure is then used that applies a normal (Gaussian) distribution to the analysis of new medical entries in order to assess the gravity of the anomalies detected. Finally, a data examination is carried out to gain insight. The results of the model and simulation show that the proposed architecture is highly efficient in applying smart technologies to a healthcare system, as an example of a research direction involving the Internet of Things, and offers a data platform that can be used for both medical decision making and the patient’s wellbeing and satisfaction with their medical treatment.
topic support vector machine
classification techniques
machine learning
blood pressure
internet of things
url https://www.mdpi.com/2076-3417/9/22/4802
work_keys_str_mv AT redachefira enhancingdecisionmakingthroughcombinedclassificationtechniquesandprobabilisticdataanalysisforubiquitoushealthcareanomalymonitoring
AT saidrakrak enhancingdecisionmakingthroughcombinedclassificationtechniquesandprobabilisticdataanalysisforubiquitoushealthcareanomalymonitoring
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