Value of fecal calprotectin in prediction of acute appendicitis based on a proposed model of machine learning

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridAkbulut, Sami/0000-0002-6864-7711
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidAkbulut, Sami/L-9568-2014
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorAkbulut, Sami
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T20:10:20Z
dc.date.available2024-08-04T20:10:20Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBACKGROUND: The aim of this study is to apply random forest (RF), one of the machine learning (ML) algorithms, to a dataset consisting of patients with a presumed diagnosis of acute appendicitis (AAp) and to reveal the most important factors associated with the diagnosis of AAp based on the variable importance. METHODS: An open-access dataset comparing two patient groups with (n=40) and without (n=44) AAp to predict biomarkers for AAp was used for this case-control study. RF was used for modeling the data set. The data were divided into two training and test dataset (80:20). Accuracy, balanced accuracy (BC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) performance metrics were appraised for model performance. RESULTS: Accuracy, BC, sensitivity, specificity, PPV, NPV, and F1 scores pertaining to the RF model were 93.8%, 93.8%, 87.5%, 100%, 100%, 88.9%, and 93.3%, respectively. Following the variable importance values regarding the model, the variables most associated with the diagnosis and prediction of AAp were fecal calprotectin (100 %), radiological imaging (89.9%), white blood test (51.8%), C-reactive protein (47.1%), from symptoms onset to the hospital visit (19.3%), patients age (18.4%), alanine aminotransferase levels >40 (<1%), fever (<1%), and nausea/vomiting (<1%), respectively. CONCLUSION: A prediction model was developed for AAp with the ML method in this study. Thanks to this model, biomarkers that predict AAp with high accuracy were determined. Thus, the decision-making process of clinicians for diagnosing AAp will be facilitated, and the risks of perforation and unnecessary operations will be minimized thanks to the timely diagnosis with high accuracy.en_US
dc.identifier.doi10.14744/tjtes.2023.10001
dc.identifier.endpage662en_US
dc.identifier.issn1306-696X
dc.identifier.issn1307-7945
dc.identifier.issue6en_US
dc.identifier.pmid37278078en_US
dc.identifier.scopus2-s2.0-85160973212en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage655en_US
dc.identifier.trdizinid1190441en_US
dc.identifier.urihttps://doi.org/10.14744/tjtes.2023.10001
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1190441
dc.identifier.urihttps://hdl.handle.net/11616/92722
dc.identifier.volume29en_US
dc.identifier.wosWOS:001008925000002en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherTurkish Assoc Trauma Emergency Surgeryen_US
dc.relation.ispartofUlusal Travma Ve Acil Cerrahi Dergisi-Turkish Journal of Trauma & Emergency Surgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAcute appendicitisen_US
dc.subjectfecal calprotectinen_US
dc.subjectmachine learningen_US
dc.subjectrandom foresten_US
dc.subjectvariable importanceen_US
dc.titleValue of fecal calprotectin in prediction of acute appendicitis based on a proposed model of machine learningen_US
dc.typeArticleen_US

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