Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians

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dc.authoridyazici, hilmi/0000-0001-7470-0518
dc.authoridAygul, Yesim/0000-0003-0605-9604
dc.authoridYILDIRIM, MEHMET/0000-0003-4768-4537
dc.contributor.authorYazici, Hilmi
dc.contributor.authorUgurlu, Onur
dc.contributor.authorAygul, Yesim
dc.contributor.authorUgur, Mehmet Alperen
dc.contributor.authorSen, Yigit Kaan
dc.contributor.authorYildirim, Mehmet
dc.date.accessioned2025-03-20T09:50:45Z
dc.date.available2025-03-20T09:50:45Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractBackgrounds Acute Appendicitis (AA) is one of the most common surgical emergencies worldwide. This study aims to investigate the predictive performances of 6 different Machine Learning (ML) algorithms for simple and complicated AA. Methods Data regarding operated AA patients between 2012 and 2022 were analyzed retrospectively. Based on operative findings, patients were evaluated under two groups: perforated AA and none-perforated AA. The features that showed statistical significance (p < 0.05) in both univariate and multivariate analysis were included in the prediction models as input features. Five different error metrics and the area under the receiver operating characteristic curve (AUC) were used for model comparison. Results A total number of 1132 patients were included in the study. Patients were divided into training (932 samples), testing (100 samples), and validation (100 samples) sets. Age, gender, neutrophil count, lymphocyte count, Neutrophil to Lymphocyte ratio, total bilirubin, C-Reactive Protein (CRP), Appendix Diameter, and PeriAppendicular Liquid Collection (PALC) were significantly different between the two groups. In the multivariate analysis, age, CRP, and PALC continued to show a significant difference in the perforated AA group. According to univariate and multivariate analysis, two data sets were used in the prediction model. K-Nearest Neighbors and Logistic Regression algorithms achieved the best prediction performance in the validation group with an accuracy of 96%. Conclusion The results showed that using only three input features (age, CRP, and PALC), the severity of AA can be predicted with high accuracy. The developed prediction model can be useful in clinical practice. Highlights ML models can be used in all parts of medical treatments. With good features, it would be useful in the prediction of surgical pathologies. ML models are strong predictors of the severity of acute appendicitis. With simple and easily found tools, the Logistic Regression algorithm predicted the severity of acute appendicitis with 96% accuracy. This study also used an unseen data set to validate the results of training data. This increased the reliability of the prediction models.
dc.identifier.doi10.1186/s12873-024-01023-9
dc.identifier.issn1471-227X
dc.identifier.issue1
dc.identifier.pmid38886641
dc.identifier.scopus2-s2.0-85196109255
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1186/s12873-024-01023-9
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2289
dc.identifier.volume24
dc.identifier.wosWOS:001250988900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBMC
dc.relation.ispartofBmc Emergency Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectAppendicitis
dc.subjectMachine learning
dc.subjectPrediction
dc.subjectSeverity
dc.titlePredicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians
dc.typeArticle

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