Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainab...
Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence
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Publisher
Switzerland: MDPI AG
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Language
English
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Publisher
Switzerland: MDPI AG
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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).
A total of 1797 patients who underwent appendectomy with a preliminary di...
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Full title
Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence
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TN_cdi_doaj_primary_oai_doaj_org_article_d32a866238334db7b4d46ab2bf53a680
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d32a866238334db7b4d46ab2bf53a680
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ISSN
2075-4418
E-ISSN
2075-4418
DOI
10.3390/diagnostics13061173
