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Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

Mcheick Hamid, Saleh Lokman, Ajami Hicham et Mili Hafedh. (2017). Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network. Sensors, 17, (7),

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URL officielle: http://dx.doi.org/doi:10.3390/s17071486

Résumé

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%).

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:17
Numéro:7
Version évaluée par les pairs:Oui
Date:23 Juin 2017
Sujets:Sciences naturelles et génie > Sciences mathématiques > Informatique
Sciences naturelles et génie > Sciences mathématiques > Mathématiques appliquées
Département, module, service et unité de recherche:Départements et modules > Département d'informatique et de mathématique
Mots-clés:context-aware applications, health care system, Bayesian Belief Network, ubiquitous and ambient computing, chronic pulmonary disease, applications sensibles au contexte, système de santé, informatique omniprésente et ambiante, maladie pulmonaire chronique
Déposé le:01 nov. 2018 01:14
Dernière modification:01 nov. 2018 01:14
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Creative Commons LicenseSauf indication contraire, les documents archivés dans Constellation sont rendus disponibles selon les termes de la licence Creative Commons "Paternité, pas d'utilisation commerciale, pas de modification" 2.5 Canada.

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