A diagnostic testing for people with appendicitis using machine learning techniques

Abstract

Appendicitis is a common disease that occurs particularly often in childhood and adolescence. The accurate diagnosis of acute appendicitis is the most significant precaution to avoid severe unnecessary surgery. In this paper, the author presents a machine learning (ML) technique to predict appendix illness whether it is acute or subacute, especially between 10 and 30 years and whether it requires an operation or just taking medication for treatment. The dataset has been collected from public hospital-based citizens between 2016 and 2019. The predictive results of the models achieved by different ML techniques (Logistic Regression, Naïve Bayes, Generalized Linear, Decision Tree, Support Vector Machine, Gradient Boosted Tree, Random Forest) are compared. The covered dataset are 625 specimens and the total of the medical records that are applied in this paper include 371 males (60.22%) and 254 females (40.12%). According to the dataset, the records consist of 318 (50.88%) operated and 307 (49.12%) unoperated patients. It is observed that the random forest algorithm obtains the optimal result with an accurately predicted result of 83.75%, precision of 84.11%, sensitivity of 81.08%, and the specificity of 81.01%. Moreover, an estimation method based on ML techniques is improved and enhanced to detect individuals with acute appendicitis.