Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/454
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dc.contributor.authorSaleh, Lokmanen_US
dc.contributor.authorMcheick, Hamiden_US
dc.contributor.authorAjami, Hichamen_US
dc.contributor.authorMili, Hafedhen_US
dc.contributor.authorDargham, Joumanaen_US
dc.date.accessioned2020-12-23T08:30:45Z-
dc.date.available2020-12-23T08:30:45Z-
dc.date.issued2017-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/454-
dc.description.abstractMedicine and especially chronic diseases, like everything else on earth is filled with ambiguity. This is why, identifying patients at risk present a big challenge to human brain. Poor control and misdiagnosis of chronic diseases has a great impact quality of life of patients, the expenses and performance of health care system. The global economic cost of chronic diseases could reach {dollar}47 trillion by 2030, according to a study by the World Economic Forum (WEF). Beside this economic burden, such treatment failure increases the risk of progression of disease which inevitably leads to premature death or further illness and suffering. Today, health informatics is reshaping the research in the medical domain due to its potential to concurrently overcome the challenges encountered in the traditional healthcare systems. Uncertainty, accuracy, causal attributes and their relationship, all have their places in this new technology through contemporary machine learning algorithms. Prediction of exacerbation of Chronic Obstructive Pulmonary Disease (COPD) is considered one of the most difficult problems in the medical field. In this paper, we will leverage unused machine learning methods to increase prediction accuracy in COPD. To this end, we compared three of the most common machine learning algorithms (decision tree, naive Bayes and Bayesian network) based on ROC metric. Furthermore, we used discretization process for the first time in this context.en_US
dc.language.isoengen_US
dc.subjectChronic Pulmonary Disease COPDen_US
dc.subjectExacerbationen_US
dc.subjectSelect relevant attributesen_US
dc.subjectAccuracyen_US
dc.subject.lcshMachine learningen_US
dc.subject.lcshPrediction (Logic)en_US
dc.titleComparison of machine learning algorithms to increase prediction accuracy of COPD domainen_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Smart Homes and Health Telematics (29-31 August 2017 : Paris, France)en_US
dc.contributor.affiliationDepartment of Computer Scienceen_US
dc.description.startpage247en_US
dc.description.endpage254en_US
dc.date.catalogued2019-02-26-
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=https://link.springer.com/chapter/10.1007/978-3-319-66188-9_22en_US
dc.identifier.OlibID190190-
dc.relation.ispartoftextEnhanced Quality of Life and Smart Livingen_US
dc.provenance.recordsourceOliben_US
crisitem.author.parentorgFaculty of Arts and Sciences-
Appears in Collections:Department of Computer Science
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