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

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridARSLAN, Ahmet Kadir/0000-0001-8626-9542
dc.authoridGÜLDOĞAN, Emek/0000-0002-5436-8164
dc.authoridErdil, Nevzat/0000-0002-8275-840X
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidARSLAN, Ahmet Kadir/AAA-2409-2020
dc.authorwosidGÜLDOĞAN, Emek/ABH-5460-2020
dc.authorwosidColak, M. Cengiz/ABI-3394-2020
dc.authorwosidErdil, Nevzat/K-8079-2019
dc.contributor.authorGuldogan, Emek
dc.contributor.authorArslan, Ahmet Kadir
dc.contributor.authorColak, M. Cengiz
dc.contributor.authorColak, Cemil
dc.contributor.authorErdil, Nevzat
dc.date.accessioned2024-08-04T20:56:27Z
dc.date.available2024-08-04T20:56:27Z
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, Naive 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.en_US
dc.identifier.endpage1556en_US
dc.identifier.issn0970-938X
dc.identifier.issn0976-1683
dc.identifier.issue4en_US
dc.identifier.startpage1553en_US
dc.identifier.urihttps://hdl.handle.net/11616/102325
dc.identifier.volume28en_US
dc.identifier.wosWOS:000396830900019en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherAllied Acaden_US
dc.relation.ispartofBiomedical Research-Indiaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBayesian networken_US
dc.subjectNaive 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 discovery.en_US
dc.typeArticleen_US

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