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

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.

Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis

“…Our 10-fold CV results ( Table 3 ) are overall comparable to the performance levels reported by Reismann et al ( 40 ), Akmese et al ( 41 ), Aydin et al ( 42 ), and Stiel et al ( 43 ) whose studies are similar to ours. Compared to the previous work on using ML to predict pediatric appendicitis ( 40 – 43 ), our analysis considers the most extensive set of variables and, to the best of our knowledge, is the first to simultaneously predict diagnosis, management, and severity of appendicitis in pediatric patients.…”

“…In their analysis, gradient boosting attained the highest accuracy (95%). Similar to Akmese et al ( 41 ) Aydin et al detected pediatric appendicitis based on demographic and pre-operative laboratory data ( 42 ). In addition, they differentiated between complicated and uncomplicated appendicitis.…”

“…They analyzed laboratory variables and appendix diameter in US and achieved AUROCs of 0.91 and 0.80 for diagnosing appendicitis and differentiating complicated appendicitis, respectively. Akmese et al analyzed demographic and laboratory data and used a range of ML methods to predict whether pediatric patients with suspected acute appendicitis underwent surgery ( 41 ). In their analysis, gradient boosting attained the highest accuracy (95%).…”

Acute appendicitis–advances and controversies

“…DT analysis based on rebound tenderness severity, pain migration, urinalysis, symptom duration, leukocytosis, neutrophil levels, and CRP was more accurate (receiver operating characteristic and AUC 0.85) as compared to the Alvarado score (AUC 0.695), the Eskelinen score (AUC 0.715), and the AAS (AUC 0.749)[ 59 ]. In a study by Akmese et al [ 60 ] involving 595 clinical records, a boosted tree algorithm based on demographic data and serum biochemistry had predicted surgery necessity with 95.3% accuracy[ 60 ]. However, due to the retrospective nature, the subjective clinical judgment of the surgeon could influence the results.…”

A diagnostic testing for people with appendicitis using machine learning techniques

“…The results obtained by this study have proved the accuracy of the biomarker signature for diagnosis of appendicitis is 90%, while the accuracy to perfectly identify complicated inflammation is 51% on validation data. The closest study to the current study is that by Akmese et al [3] when the data consisted of 595 medical records and machine learning techniques are applied to predict appendicitis disease and also determine whether or not surgery is needed. The accuracy in this study is 95.31% by the gradient enhancement algorithm.…”