Abstract
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.
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Kim, E., Subhas, G., Mittal, V.K., Golladay, E.S.: C-reactive protein estimation does not improve accuracy in the diagnosis of acute appendicitis in pediatric patients. Int. J. Surg. 7(1), 74–77 (2009)
Fergusson, J., Hitos, K., Simpson, E.: Utility of white cell count and ultrasound in the diagnosis of acute appendicitis. ANZ. J. Surg. 72(11), 781–785 (2002)
Petroianu, A.: Diagnosis of acute appendicitis. Int. J. Surg. 10(3), 115–119 (2012)
Alvarado, A.: A practical score for the early diagnosis of acute appendicitis. Ann. Emerg. Med. 15(5), 557–564 (1986)
Pritchett, C., Levinsky, N., Ha, Y., Dembe, A., Steinberg, S.: Management of acute appendicitis: the impact of CT scanning on the bottom line. J. Am. Coll. Surg. 210(5), 699–705 (2010)
Yang, H., Wang, Y., Chung, P., Chen, W., Jeng, L., Chen, R.: Laboratory tests in patients with acute appendicitis. ANZ. J. Surg. 76(1-2), 71–74 (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer (2010)
Hu, X., Cammann, H., Meyer, H., Miller, K., Jung, K., Stephan, C.: Artificial neural networks and prostate cancer-tools for diagnosis and management. Nat. Rev. Urol. 10, 1–9 (2013)
Mat-Isa, N., Mashor, M., Othman, N.: An automated cervical pre-cancerous diagnostic system. Artif. Intell. Med. 42(1), 1–11 (2008)
Shi, H., Tsai, J., Chen, Y., Culbertson, R., Chang, H., Hou, M.: Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models. Breast Cancer Res. Treat. 135(1), 221–229 (2012)
Hale, D., Molloy, M., Pearl, R., Schutt, D., Jaques, D.: Appendectomy: a contemporary appraisal. Ann. Surg. 225(3), 252–261 (1997)
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)
de Dombal, F., Leaper, D., Staniland, J., McCann, A., Horrocks, J.: Computer-aided diagnosis of acute abdominal pain. Br. Med. J. 2(5804), 9–13 (1972)
Chen, L., Xuan, J., Riggins, R., Clarke, R., Wang, Y.: Identifying cancer biomarkers by network-constrained support vector machines. BMC Syst. Biol. 5, 161 (2011)
Mourao-Miranda, J., Reinders, A., Rocha-Rego, V., Lappin, J., Rondina, J., Morgan, C., et al.: Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study. Psychol. Med. 42(5), 1037–1047 (2012)
Lancashire, L., Roberts, D., Dive, C., Renehan, A.: The development of composite circulating biomarker models for use in anticancer drug clinical development. Int. Cancer 128(8), 1843–1851 (2011)
Lv, G., Cheng, H., Zhai, H., Dong, L.: Fault diagnosis of power transformer based on multi-layer SVM classifier. Electr. Power Syst. Res. 75(1), 9–15 (2005)
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Park, S.Y., Seo, J.S., Lee, S.C., Kim, S.M. (2014). Application of an Artificial Intelligence Method for Diagnosing Acute Appendicitis: The Support Vector Machine. In: Park, J., Stojmenovic, I., Choi, M., Xhafa, F. (eds) Future Information Technology. Lecture Notes in Electrical Engineering, vol 276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40861-8_13
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DOI: https://doi.org/10.1007/978-3-642-40861-8_13
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