The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis
Yükleniyor...
Dosyalar
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Hindawi Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
AKMESE, OMER FARUK/0000-0002-5877-0177; Erbay, Hasan/0000-0002-7555-541X
Anahtar Kelimeler
Kaynak
Emergency Medicine International
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
2020
Sayı
Künye
Akmeşe, Ö. F., Doğan, G., Kör, H., Erbay, H., & Demir, E. (2020). The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis. Emergency Medicine International, 2020(7306435), 1–8.
