A hybrid machine learning approach to improve the diagnostic accuracy of acute appendicitis

Acute appendicitis is the most common diagnosis considered in patients presenting to the emergency department with right lower quadrant pain. However, atypical presentations often lead to unnecessary surgeries and increased healthcare costs. This study aimed to improve diagnostic accuracy in acute appendicitis using a hybrid machine learning (ML) model.

A retrospective analysis was performed on 395 patients who underwent appendectomy for suspected acute appendicitis between 2020 and 2024 at Ankara University Faculty of Medicine, Department of General Surgery. Demographic, clinical, laboratory, and radiological variables were collected. ML algorithms, including NaiveBayes, MultilayerPerceptron, IBk, AdaBoost, RandomForest, and a hybrid model combining NaiveBayes, AdaBoost, and RandomForest, were applied. The dataset was evaluated using 10-fold cross-validation, repeated 1,000 times. Accuracy, F-measure, Matthews Correlation Coefficient (MCC), receiver operating characteristic (ROC) area, and precision-recall curve (PRC) area were used as performance criteria.

Among the 395 patients, 52.9% were male, with a mean age of 37.3+-15.6 years. Histopathological examination confirmed acute appendicitis in 341 (86.3%) patients and negative appendectomy in 54 (13.7%) patients. The diagnostic accuracy of the Alvarado score at a cut-off value of ≥6 was 79.0%. Among the ML algorithms, the hybrid model achieved the best performance, with 92.9% accuracy, 93% F-measure, 70.4% MCC, 90.8% ROC area, and 93.4% PRC area. This model correctly predicted 95.6% of acute appendicitis cases and 75.9% of negative appendectomy cases.

The hybrid ML model demonstrated superior diagnostic accuracy compared to the Alvarado score for acute appendicitis. Integration of such models into clinical practice could reduce negative appendectomy rates and enhance patient management by enabling faster and more reliable diagnosis.