Comparison and Analysis of Machine Learning Techniques for the Prediction of Acute Appendicitis

Abstract

Appendicitis is the most serious medical emergency requiring surgery for removing the appendix. Appendicitis treatment needs physical examination accompanied by blood tests and imaging scans to better detect signs of appendicitis or to rule out potential causes of the symptoms. Diagnosing appendicitis can be difficult because of the proximity of the appendix to other pelvic organs and its location, thus its symptoms have a tendency to overlap with other illnesses. The aim of the current study is to compare and analyze the performance of machine learning (ML) techniques in the prediction of appendicitis accurately. In the current paper, three machine learning techniques namely Support Vector Machine (SVM), Decision Tree and K-nearest Neighbor (KNN) have been taken. The experiments were carried out on the benchmark dataset of Appendicitis consisting of 590 patients. The performance of these ML techniques has been evaluated on the basis of three measures i.e. Accuracy, Recall, and Precision. The experimental result revealed that the Decision Tree algorithm performed better with an accuracy of 73.72%, Precision of 75.35%, and Recall of 68.64% as compared to SVM and KNN. It can be inferred from the experimental results that models based on machine learning techniques can predict appendicitis accurately and can serve as a decision-making aid by providing a correct and timely diagnosis of appendicitis, thereby reducing the negative appendectomy rate.

Keywords: Appendicitis, Decision tree, K-Nearest neighbor, Machine learning, Support vector machine.

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