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逻辑回归与人工神经网络分类模型:方法学综述

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

作者信息

Dreiseitl Stephan, Ohno-Machado Lucila

机构信息

Department of Software Engineering for Medicine, Upper Austria University of Applied Sciences, Hagenberg, Austria.

出版信息

J Biomed Inform. 2002 Oct-Dec;35(5-6):352-9. doi: 10.1016/s1532-0464(03)00034-0.

Abstract

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.

摘要

逻辑回归和人工神经网络是许多医学数据分类任务中的首选模型。在本综述中,我们从技术角度总结了这些模型的异同,并将它们与其他机器学习算法进行比较。我们提供了有助于批判性评估模型质量以及基于这些模型得出的结果的考量因素。最后,我们总结了在医学文献样本中,逻辑回归和人工神经网络模型的质量标准是如何得到满足的研究结果。

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