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

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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.

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Acknowledgements

We thank Ms. Akane Inaizumi, Ms. Atsuko Sugiyama and Mr. Toshikazu Abe for valuable assistance in the acquisition of the data and references. We also thank Dr. Ralph Grams for his kind suggestions.

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Authors and Affiliations

  1. Division of Information Science and Biostatistics, Niigata University Graduate School of Medical and Dental Sciences, Asahimachi-Dori 1-754, Niigata, 951-8520, Japan

    Shinya Sakai, Kuriko Kobayashi & Nozomu Mandai

  2. Department of Medical Informatics, Niigata University Medical and Dental Hospital, Asahimachi-Dori 1-754, Niigata, 951-8520, Japan

    Shin-ichi Toyabe & Kohei Akazawa

  3. Division of Digestive and General Surgery, Niigata University Graduate School of Medical and Dental Sciences, Asahimachi-Dori 1-754, Niigata, 951-8520, Japan

    Tatsuo Kanda

Authors
  1. Shinya Sakai
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  2. Kuriko Kobayashi
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  3. Shin-ichi Toyabe
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  4. Nozomu Mandai
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  5. Tatsuo Kanda
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  6. Kohei Akazawa
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Corresponding author

Correspondence to Kohei Akazawa.

Additional information

The MATLAB program, which we used to construct the ANN models, is available for any interested individuals. Please contact via e-mail: toyabe@med.niigata-u.ac.jp.

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Sakai, S., Kobayashi, K., Toyabe, Si. et al. Comparison of the Levels of Accuracy of an Artificial Neural Network Model and a Logistic Regression Model for the Diagnosis of Acute Appendicitis. J Med Syst 31, 357–364 (2007). https://doi.org/10.1007/s10916-007-9077-9

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  • DOI: https://doi.org/10.1007/s10916-007-9077-9

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