Logistic regression and artificial neural network classification models: a methodology review

被引:1431
作者
Dreiseitl, S [1 ]
Ohno-Machado, L
机构
[1] Upper Austria Univ Appl Sci, Dept Software Engn Med, Hagenberg, Austria
[2] Harvard Univ, Brigham & Womens Hosp, Sch Med, Div Hlth Sci & Technol,Decis Syst Grp, Boston, MA 02115 USA
[3] MIT, Boston, MA USA
关键词
artificial neural networks; logistic regression; classification; model comparison; model evaluation; medical data analysis;
D O I
10.1016/S1532-0464(03)00034-0
中图分类号
TP39 [计算机的应用];
学科分类号
081203 [计算机应用技术]; 0835 [软件工程];
摘要
Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models. Finally, we summarize our findings on how quality criteria for logistic regression and artificial neural network models are met in a sample of papers from the medical literature. (C) 2003 Elsevier Science (USA). All rights reserved.
引用
收藏
页码:352 / 359
页数:8
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