Evaluation of automatic knowledge acquisition techniques in the diagnosis of acute abdominal pain
作者： C. Ohmann , V. Moustakis , Q. Yang , K. Lang , Acute Abdominal Pain Study Group
DOI: 10.1016/0933-3657(95)00018-6
关键词: Knowledge acquisition 、 Computer-aided diagnosis 、 Medicine 、 ID3 、 Rule induction 、 Artificial intelligence 、 Sample size determination 、 Medical diagnosis 、 MEDLINE 、 Machine learning 、 Bayes' theorem
摘要: Clinical diagnosis in acute abdominal pain is still a major problem. Computer-aided offers some help; however, existing systems produce high error rates. We therefore tested machine learning techniques order to improve standard statistical systems. The investigation was based on prospective clinical database with 1254 cases, 46 diagnostic parameters and 15 diagnoses. Independence Bayes the automatic rule induction ID3, NewId, PRISM, CN2, C4.5 ITRULE were trained 839 cases separately 415 cases. No differences overall accuracy observed (43-48%), except for which below average. Between different similarities found, but also considerable respect specific Machine did not results of model Bayes. Problem dimensionality, sample size complexity are factors influencing computer-aided pain.
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