The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis

Omer F Akmese; Gul Dogan; Hakan Kor; Hasan Erbay; Emre Demir

doi:10.1155/2020/7306435

The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis

Emerg Med Int. 2020 Apr 25:2020:7306435. doi: 10.1155/2020/7306435. eCollection 2020.

Authors

Omer F Akmese¹, Gul Dogan², Hakan Kor³, Hasan Erbay⁴, Emre Demir⁵

Affiliations

¹ Department of Computer Technologies, University of Hitit, University of Kırıkkale, Çorum 19500, Turkey.
² Department of Surgical Medical Sciences, University of Hitit, Çorum 19040, Turkey.
³ Department of Computer Technologies, University of Hitit, Çorum 19300, Turkey.
⁴ Department of Computer Engineering, University of Turkish Aeronautical Association, Ankara 06790, Turkey.
⁵ Department of Biostatistics, University of Hitit, Çorum 19040, Turkey.

Abstract

Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.