Application of an Artificial Intelligence Method for Diagnosing Acute Appendicitis: The Support Vector Machine
The aim of this study is to suggest an artificial intelligence model to diagnosis acute appendicitis using a support vector machine (SVM). Acute appendicitis is one of the most common abdominal surgery emergencies. Various methods have been developed to diagnose appendicitis, but they have not performed well in the Middle East, Asia, or the West. A total of 760 patients were used to construct the SVM. Both the Alvarado clinical scoring system (ACSS) and multilayer neural networks (MLNN) were used to compare performance. The accuracies of the ACSS, MLNN, and SVM were 54.87%, 92.89, and 99.61%, respectively. The areas under the curve of ACSS, MLNN, and SVM were 0.621, 0.969, and 0.997 respectively. The performance of the AI model was significantly better than that of the ACSS (P < 0.001). We consider that the developed models are a useful method to reduce both negative appendectomies and delayed diagnoses, particularly for junior clinical surgeons.
Keywordsappendicitis artificial intelligence support vector machine clinical scoring system a receiver operating characteristics graph
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- 7.Vapnik, V.: The Nature of Statistical Learning Theory. Springer (2010)Google Scholar
- 12.Pouget-Baudry, Y., Mucci, S., Eyssartier, E., Guesdon-Portes, A., Lada, P., Casa, C., et al.: The use of the Alvarado score in the management of right lower quadrant abdominal pain in the adult. J. Visc. Surg. 147(2), e40–e44 (2010)Google Scholar