research-article

HCES: helper context engine system to predict relevant state of patients in COPD domain using naïve bayesian

Authors:

Hafedh MiliAuthors Info & Claims

IML '17: Proceedings of the 1st International Conference on Internet of Things and Machine Learning

Article No.: 55, Pages 1 - 10

https://doi.org/10.1145/3109761.3109792

Published: 17 October 2017 Publication History

Abstract

Context-aware¹ systems have been employed to help users in their daily lives. In the recent years, researchers are exploring how context aware systems can benefit humanity through assisting patients, specifically those who suffer incurable diseases, to cope with their illness. In this paper, we direct our work to help people who suffer from COPD. Existing solutions have limits in terms of choosing relevant parameters and the accuracy of detecting exacerbations in the COPD. We propose HCES (Helper Context-aware Engine System) that aims to support chronic disease patients and doctors on any level of disease risk. To accomplish this system, HCES adopts ontology for representing and modeling context to well understand the scenario that we can use to monitor COPD's patients, uses an efficacy algorithm to select relevant attributes instead of a no cited one, predicts exacerbations with high accuracy by adding the discretization step, and arranges the pertinent attributes to use the most pertinent in an emergency case. These steps are beneficial in handling the uncertainty of context-aware systems. Furthermore, a comparison between algorithms for selecting attributes is done to choose the best one for COPD diseases. The main goal of this paper is to help medical personnel (patients, doctors or nurses) to take an efficient decision by identifying the most context-relevant attributes and predicting patient exacerbations.

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Cited By

Ng ZLing LChew HLau Y(2021)The role of artificial intelligence in enhancing clinical nursing care: A scoping reviewJournal of Nursing Management10.1111/jonm.1342530:8(3654-3674)Online publication date: 13-Aug-2021
https://doi.org/10.1111/jonm.13425
Ahammad SRajesh VRahman MLay-Ekuakille A(2020)A Hybrid CNN-Based Segmentation and Boosting Classifier for Real Time Sensor Spinal Cord Injury DataIEEE Sensors Journal10.1109/JSEN.2020.299287920:17(10092-10101)Online publication date: 1-Sep-2020
https://doi.org/10.1109/JSEN.2020.2992879
Mcheick HSaleh LAjami HMili H(2017)Context Relevant Prediction Model for COPD Domain Using Bayesian Belief NetworkSensors10.3390/s1707148617:7(1486)Online publication date: 23-Jun-2017
https://doi.org/10.3390/s17071486

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cover image ACM Other conferences

IML '17: Proceedings of the 1st International Conference on Internet of Things and Machine Learning

October 2017

581 pages

ISBN:9781450352437

DOI:10.1145/3109761

General Chairs:
Hani Hamdan
University of Paris-Saclay, Paris, France
,
Djallel Eddine Boubiche
University of Batna, Algeria
,
Program Chair:
Fanny Klett
German Workforce ADL Partnership Laboratory, Germany

Copyright © 2017 ACM.

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Published: 17 October 2017

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IML 2017: International Conference on Internet of Things and Machine Learning

October 17 - 18, 2017

Liverpool, United Kingdom

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Cited By

Ng ZLing LChew HLau Y(2021)The role of artificial intelligence in enhancing clinical nursing care: A scoping reviewJournal of Nursing Management10.1111/jonm.1342530:8(3654-3674)Online publication date: 13-Aug-2021
https://doi.org/10.1111/jonm.13425
Ahammad SRajesh VRahman MLay-Ekuakille A(2020)A Hybrid CNN-Based Segmentation and Boosting Classifier for Real Time Sensor Spinal Cord Injury DataIEEE Sensors Journal10.1109/JSEN.2020.299287920:17(10092-10101)Online publication date: 1-Sep-2020
https://doi.org/10.1109/JSEN.2020.2992879
Mcheick HSaleh LAjami HMili H(2017)Context Relevant Prediction Model for COPD Domain Using Bayesian Belief NetworkSensors10.3390/s1707148617:7(1486)Online publication date: 23-Jun-2017
https://doi.org/10.3390/s17071486

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