The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis

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dc.authoridAkmeşe, Ömer Faruk / 0000-0002-5877-0177
dc.authoridAkmeşe, Ömer Faruk / 0000-0002-5877-0177
dc.authoridErbay, Hasan / 0000-0002-7555-541X
dc.authorwosidKör, Hakan / AAG-1869-2021
dc.authorwosidDemir, Emre / AAA-8193-2020
dc.authorwosidAkmeşe, Ömer Faruk / V-8861-2017
dc.authorwosidAkmeşe, Ömer Faruk / AAN-9222-2020
dc.authorwosidErbay, Hasan / F-1093-2016
dc.contributor.authorAkmeşe, Ömer Faruk
dc.contributor.authorDoğan, Gül
dc.contributor.authorKör, Hakan
dc.contributor.authorErbay, Hasan
dc.contributor.authorDemir, Emre
dc.date.accessioned2021-11-01T15:05:10Z
dc.date.available2021-11-01T15:05:10Z
dc.date.issued2020
dc.departmentHitit Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü
dc.departmentHitit Üniversitesi, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü
dc.departmentHitit Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAcute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.
dc.identifier.citationAkmese, O. F., Dogan, G., Kor, H., Erbay, H., & Demir, E. (2020). The use of machine learning approaches for the diagnosis of acute appendicitis. Emergency medicine international, 2020.
dc.identifier.doi10.1155/2020/7306435
dc.identifier.issn2090-2840
dc.identifier.issn2090-2859
dc.identifier.pmid32377437
dc.identifier.urihttps://doi.org/10.1155/2020/7306435
dc.identifier.urihttps://hdl.handle.net/11491/7154
dc.identifier.volume2020en_US
dc.identifier.wosWOS:000531591600001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.institutionauthorAkmeşe, Ömer Faruk
dc.institutionauthorErbay, Hasan
dc.institutionauthorKör, Hakan
dc.institutionauthorDoğan, Gül
dc.institutionauthorDemir, Emre
dc.language.isoen
dc.publisherHindawi Ltd
dc.relation.ispartofEmergency Medicine International
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject[No Keywords]en_US
dc.titleThe Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis
dc.typeArticle

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