Application of an Artificial Intelligence Method for Diagnosing Acute Appendicitis: The Support Vector Machine

  • Sung Yun Park
  • Jun Seok Seo
  • Seung Chul Lee
  • Sung Min Kim
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 276)

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.

Keywords

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sung Yun Park
    • 1
  • Jun Seok Seo
    • 2
  • Seung Chul Lee
    • 2
  • Sung Min Kim
    • 1
  1. 1.Department of Medical Bio EngineeringDongguk University-SeoulSeoulRepublic of Korea
  2. 2.Department of Emergency MedicineDongguk University Ilsan Hospital, Dongguk University-SeoulSeoulRepublic of Korea

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