Abstract
The feasibility of using machine-learning techniques to screen dyspeptic patients for those at high risk of gastric cancer was demonstrated in this study. Data on 1401 dyspeptic patients over the age of 40, consisted of 85 epidemiological and clinical variables and a gold-standard diagnosis, made by upper gastrointestinal endoscopy. The diagnoses were grouped into two classes — those at high risk of having (or developing) gastric cancer and those at low risk. A machine-learning approach was used to generate a cross-validated sensitivity-specificity curve in order to assess the power of the discrimination between the two groups.
W.Z. Liu is in the School of Mathematics and Statistics, where A.P. White is an Associate Member. M.T. Hallissey is in the Department of Surgery.
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Hallissey, M.T., Allum, W.H., Jewkes, A.J., Ellis, D.J. and Fielding, J.W.L. (1990). Early detection of gastric cancer. British Medical Journal, 301, 513–515.
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Liu, W.Z. & White, A.P. (to appear). The importance of attribute selection measures in decision tree induction. Machine Learning.
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White, A.P. and Liu, W.Z. (to appear). Bias in information-based measures in decision tree induction. Machine Learning.
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© 1994 Springer-Verlag Berlin Heidelberg
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Liu, W.Z., White, A.P., Hallissey, M.T. (1994). Early screening for gastric cancer using machine learning techniques. In: Bergadano, F., De Raedt, L. (eds) Machine Learning: ECML-94. ECML 1994. Lecture Notes in Computer Science, vol 784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_81
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DOI: https://doi.org/10.1007/3-540-57868-4_81
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