Comparison of the Levels of Accuracy of an Artificial Neural Network Model and a Logistic Regression Model for the Diagnosis of Acute Appendicitis

Authors:
Shinya Sakai;Kuriko Kobayashi;Shin-Ichi Toyabe;Nozomu Mandai;Tatsuo Kanda;Kohei Akazawa
Affiliations:
Division of Information Science and Biostatistics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan 951-8520;Division of Information Science and Biostatistics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan 951-8520;Department of Medical Informatics, Niigata University Medical and Dental Hospital, Niigata, Japan 951-8520;Division of Information Science and Biostatistics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan 951-8520;Division of Digestive and General Surgery, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan 951-8520;Department of Medical Informatics, Niigata University Medical and Dental Hospital, Niigata, Japan 951-8520
Venue:
Journal of Medical Systems
Year:
2007

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Abstract

An accurate diagnosis of acute appendicitis in the early stage is often difficult, and decision support tools to improve such a diagnosis might be required. This study compared the levels of accuracy of artificial neural network models and logistic regression models for the diagnosis of acute appendicitis. Data from 169 patients presenting with acute abdomen were used for the analyses. Nine variables were used for the evaluation of the accuracy of the two models. The constructed models were validated by the ".632+ bootstrap method". The levels of accuracy of the two models for diagnosis were compared by error rate and areas under receiver operating characteristic curves. The artificial neural network models provided more accurate results than did the logistic regression models for both indices, especially when categorical variables or normalized variables were used. The most accurate diagnosis was obtained by the artificial neural network model using normalized variables.