Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence

Background: The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). Method: A tota...

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主要な著者:	Sami Akbulut, Fatma Hilal Yagin, Ipek Balikci Cicek, Cemalettin Koc, Cemil Colak, Sezai Yilmaz
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2023-03-01
シリーズ:	Diagnostics
主題:	nonperforated acute appendicitis perforated acute appendicitis predictive markers machine learning explainable artificial intelligence
オンライン･アクセス:	https://www.mdpi.com/2075-4418/13/6/1173
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