Treatment of missing data values in a neural network based decision support system for acute abdominal pain.
In this study different substitution methods for the replacement of missing data values were inspected for the use of these cases in a neural network based decision support system for acute appendicitis. The leucocyte count had the greatest number of missing values and was used in the analyses. Four different methods were compared: substituting means, random values, nearest neighbour and a neural network. There were great differences in the substituted leucocyte count values between different methods and only nearest neighbour and neural network agreed about most of the cases. The importance of the substitution method for the final diagnostic classification of the patients by the neural network based decision support system was found to be small.
Department of Computer Science and Applied Mathematics, University of Kuopio, Finland. Erkki.Pesonen@uku.fi
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Pesonen
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Eskelinen
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Juhola
D015746:Abdominal Pain / Q000175:diagnosis*; D001064:Appendicitis / Q000175:diagnosis; D003625:Data Collection; D020000:Decision Support Systems, Clinical; D006801:Humans; D016571:Neural Networks, Computer; D015203:Reproducibility of Results