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Title: Comparison of machine learning algorithms to increase prediction accuracy of COPD domain
Authors: Saleh, Lokman
Mcheick, Hamid
Ajami, Hicham
Mili, Hafedh
Dargham, Joumana 
Affiliations: Department of Computer Science 
Keywords: Chronic Pulmonary Disease COPD
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Subjects: Machine learning
Prediction (Logic)
Issue Date: 2017
Part of: Enhanced Quality of Life and Smart Living
Start page: 247
End page: 254
Conference: International Conference on Smart Homes and Health Telematics (29-31 August 2017 : Paris, France) 
Medicine 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.
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Type: Conference Paper
Appears in Collections:Department of Computer Science

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