D J Sargent1. 1. Cancer Center Statistics, Mayo Clinic, Rochester, Minnesota 55905, USA. sargent.dan@mayo.edu
Abstract
BACKGROUND: In recent years, considerable attention has been given to the development of sophisticated techniques for exploring data sets. One such class of techniques is artificial neural networks (ANNs). Artificial neural networks have many attractive theoretic properties, specifically, the ability to detect non predefined relations such as nonlinear effects and/or interactions. These theoretic advantages come at the cost of reduced interpretability of the model output. Many authors have analyzed the same data set, based on these factors, with both standard statistical methods (such as logistic or Cox regression) and ANN. METHODS: The goal of this work is to review the literature comparing the performance of ANN with standard statistical techniques when applied to medium to large data sets (sample size > 200 patients). A thorough literature search was performed, with specific criteria for a published comparison to be included in this review. RESULTS: In the 28 studies included in this review, ANN outperformed regression in 10 cases (36%), was outperformed by regression in 4 cases (14%), and the 2 methods had similar performance in the remaining 14 cases (50%). However, in the 8 largest studies (sample size > 5000), regression and ANN tied in 7 cases, with regression winning in the remaining case. In addition, there is some suggestion of publication bias. CONCLUSIONS: Neither method achieves the desired performance. Both methods should continue to be used and explored in a complementary manner. However, based on the available data, ANN should not replace standard statistical approaches as the method of choice for the classification of medical data. Copyright 2001 American Cancer Society.
BACKGROUND: In recent years, considerable attention has been given to the development of sophisticated techniques for exploring data sets. One such class of techniques is artificial neural networks (ANNs). Artificial neural networks have many attractive theoretic properties, specifically, the ability to detect non predefined relations such as nonlinear effects and/or interactions. These theoretic advantages come at the cost of reduced interpretability of the model output. Many authors have analyzed the same data set, based on these factors, with both standard statistical methods (such as logistic or Cox regression) and ANN. METHODS: The goal of this work is to review the literature comparing the performance of ANN with standard statistical techniques when applied to medium to large data sets (sample size > 200 patients). A thorough literature search was performed, with specific criteria for a published comparison to be included in this review. RESULTS: In the 28 studies included in this review, ANN outperformed regression in 10 cases (36%), was outperformed by regression in 4 cases (14%), and the 2 methods had similar performance in the remaining 14 cases (50%). However, in the 8 largest studies (sample size > 5000), regression and ANN tied in 7 cases, with regression winning in the remaining case. In addition, there is some suggestion of publication bias. CONCLUSIONS: Neither method achieves the desired performance. Both methods should continue to be used and explored in a complementary manner. However, based on the available data, ANN should not replace standard statistical approaches as the method of choice for the classification of medical data. Copyright 2001 American Cancer Society.
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