An intelligent system for the classification of postoperative pleural effusion between 4 and 30 days using medical knowledge discovery

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dc.authorscopusid57193501488
dc.authorscopusid57188684428
dc.authorscopusid6602622196
dc.authorscopusid11738942300
dc.authorscopusid6701417742
dc.contributor.authorGuldogan E.
dc.contributor.authorArslan A.K.
dc.contributor.authorColak M.C.
dc.contributor.authorColak C.
dc.contributor.authorErdil N.
dc.date.accessioned2024-08-04T19:59:38Z
dc.date.available2024-08-04T19:59:38Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: Pleural Effusion (PE) is a considerable and a common health problem. The classification of this condition is of great importance in terms of clinical decision making. The purpose of the study is to design an intelligent system for the classification of postoperative pleural effusion between 4 and 30 days after surgery by medical knowledge discovery (MKD) methods. Materials and methods: This study included 2309 individuals diagnosed with coronary artery disease for elective coronary artery bypass grafting (CABG) operation. The results of chest x-ray were used to diagnose PE. The subjects were allocated to two groups: PE group (n=81) and non-PE group (n=2228), consecutively. In the preprocessing step, outlier analysis, data transformation and feature selection processes were performed. In the data mining step, Naïve Bayes, Bayesian network and Random Forest algorithms were utilized. Accuracy and area under receiver operating characteristics (ROC) curve (AUC) were calculated as evaluation metrics. Results: In the preprocessing step, 85 outlier observations were removed from the study. The rest of the data consisted of 2224 subjects: 2149 of these individuals were in non-PE group, and the 75 were in PE group. Random Forest yielded the best classification performance with 97.45% of accuracy and 0.990 of AUC for 0.7 of the optimal split ratio by Grid search algorithm. Conclusion: The achieved results pointed out that the best classification performance was obtained from the RF ensemble model. Therefore, the suggested intelligent system can be used as a clinical decision making tool. © 2017, Scientific Publishers of India. All rights reserved.en_US
dc.identifier.endpage1556en_US
dc.identifier.issn0970-938X
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85014326676en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1553en_US
dc.identifier.urihttps://hdl.handle.net/11616/90773
dc.identifier.volume28en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherScientific Publishers of Indiaen_US
dc.relation.ispartofBiomedical Research (India)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBayesian networken_US
dc.subjectNaïve bayesen_US
dc.subjectPleural effusionen_US
dc.subjectRandom foresten_US
dc.subjectRisk factorsen_US
dc.titleAn intelligent system for the classification of postoperative pleural effusion between 4 and 30 days using medical knowledge discoveryen_US
dc.typeArticleen_US

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