A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children.

Journal: Pediatric surgery international
Published Date:

Abstract

INTRODUCTION: There is a tendency toward nonoperative management of appendicitis resulting in an increasing need for preoperative diagnosis and classification. For medical purposes, simple conceptual decision-making models that can learn are widely used. Decision trees are reliable and effective techniques which provide high classification accuracy. We tested if we could detect appendicitis and differentiate uncomplicated from complicated cases using machine learning algorithms.

Authors

  • Emrah Aydin
    Department of Pediatric Surgery, Koç University School of Medicine, Istanbul, Turkey. dremrahaydin@yahoo.com.
  • İnan Utku Türkmen
    Applied Data Science, TED University, Ankara, Turkey.
  • Gözde Namli
    Department of Pediatric Surgery, Bahcelievler State Hospital, Istanbul, Turkey.
  • Çiğdem Öztürk
    Department of Pathology, Bagcilar Training and Research Hospital, Istanbul, Turkey.
  • Ayşe B Esen
    Department of Microbiology, Bagcilar Training and Research Hospital, Istanbul, Turkey.
  • Y Nur Eray
    Department of Pediatric Surgery, Bagcilar Training and Research Hospital, Istanbul, Turkey.
  • Egemen Eroğlu
    Department of Pediatric Surgery, Koç University School of Medicine, Istanbul, Turkey.
  • Fatih Akova
    Department of Pediatric Surgery, Bagcilar Training and Research Hospital, Istanbul, Turkey.