Decision Support Proposal for Imbalanced Clinical Data

Abstract

The difficult diagnosis of acute appendicitis of patients appealing to the hospital with abdominal pain often leads to unnecessary acute appendicitis operations. Accordingly, the aim of this study is to be able to provide the correct diagnosis whether the existing case indeed necessitates operation or not through machine learning algorithms based on classification. To that purpose, SMOTE, random oversampling, and random undersampling methods were proposed to reduce the negative effects of imbalanced data set problem on classification, and it was benefitted from the risk factors in relation to Alvarado Score to predict the diagnosis of acute appendicitis. Additionally, a classification model was generated by using support vector machine classification algorithm. A decision support system was developed that could contribute to the decision making by generating interface for support vector machine algorithm in which the best performance was obtained.