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- W2128905973 abstract "The aim of this work is to compare the performance of simple neural networks with that of statistical Gaussian classifiers in the discrimination of normal- and high-risk post-operative cardiac patients. The considered data include 13 cardio-respiratory quantities taken from 158 patients. Feature extraction techniques were applied in order to extract the 3 variables out of 13 more suitable for classification. The results indicate that there are combinations of three original variables which allow better discrimination between normal and high-risk patients by neural networks than by a Gaussian classifier. For instance, using the cardiac index, the left ventricle stroke work index and oxygen pressure in mixed venous blood, a linear perceptron can discriminate patients with an error as low as 6.9%. Application of the Karhunen-Loeve transform allows the classification error to be reduced to 5.1% with both neural and statistical classifiers if a non-linear discriminant function is used." @default.
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- W2128905973 date "2002-11-19" @default.
- W2128905973 modified "2023-09-28" @default.
- W2128905973 title "Classification of postoperative cardiac patients by means of simple neural networks" @default.
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- W2128905973 doi "https://doi.org/10.1109/cic.1995.482778" @default.
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