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Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network
In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) h...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539774/ https://www.ncbi.nlm.nih.gov/pubmed/28644419 http://dx.doi.org/10.3390/s17071486 |
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author | Mcheick, Hamid Saleh, Lokman Ajami, Hicham Mili, Hafedh |
author_facet | Mcheick, Hamid Saleh, Lokman Ajami, Hicham Mili, Hafedh |
author_sort | Mcheick, Hamid |
collection | PubMed |
description | In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%). |
format | Online Article Text |
id | pubmed-5539774 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-55397742017-08-11 Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network Mcheick, Hamid Saleh, Lokman Ajami, Hicham Mili, Hafedh Sensors (Basel) Article In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%). MDPI 2017-06-23 /pmc/articles/PMC5539774/ /pubmed/28644419 http://dx.doi.org/10.3390/s17071486 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mcheick, Hamid Saleh, Lokman Ajami, Hicham Mili, Hafedh Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network |
title | Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network |
title_full | Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network |
title_fullStr | Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network |
title_full_unstemmed | Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network |
title_short | Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network |
title_sort | context relevant prediction model for copd domain using bayesian belief network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539774/ https://www.ncbi.nlm.nih.gov/pubmed/28644419 http://dx.doi.org/10.3390/s17071486 |
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