Application of an artificial intelligence method for diagnosing acute appendicitis: The support vector machine

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7 Scopus citations

Abstract

The aim of this study is to suggest an artificial intelligence model to diagnosis acute appendicitis using a support vector machine (SVM). Acute appendicitis is one of the most common abdominal surgery emergencies. Various methods have been developed to diagnose appendicitis, but they have not performed well in the Middle East, Asia, or the West. A total of 760 patients were used to construct the SVM. Both the Alvarado clinical scoring system (ACSS) and multilayer neural networks (MLNN) were used to compare performance. The accuracies of the ACSS, MLNN, and SVM were 54.87%, 92.89, and 99.61%, respectively. The areas under the curve of ACSS, MLNN, and SVM were 0.621, 0.969, and 0.997 respectively. The performance of the AI model was significantly better than that of the ACSS (P < 0.001). We consider that the developed models are a useful method to reduce both negative appendectomies and delayed diagnoses, particularly for junior clinical surgeons.

Original languageEnglish
Title of host publicationFuture Information Technology, FutureTech 2013
PublisherSpringer Verlag
Pages85-92
Number of pages8
ISBN (Print)9783642408601
DOIs
StatePublished - 2014
Event8th FTRA International Conference on Future Information Technology, FutureTech 2013 - Gwangju, Korea, Republic of
Duration: 4 Sep 20136 Sep 2013

Publication series

NameLecture Notes in Electrical Engineering
Volume276 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference8th FTRA International Conference on Future Information Technology, FutureTech 2013
Country/TerritoryKorea, Republic of
CityGwangju
Period4/09/136/09/13

Keywords

  • a receiver operating characteristics graph
  • appendicitis
  • artificial intelligence
  • clinical scoring system
  • support vector machine

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