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

@article{citeulike:3048420, 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.}, author = {Dreiseitl, Stephan and Ohno-Machado, Lucila}, citeulike-article-id = {3048420}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S1532-0464(03)00034-0}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1532-0464(03)00034-0}, citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6WHD-48SP6F0-1/1/5d544c500727dd53c2fd14b9ae1a491d}, doi = {10.1016/S1532-0464(03)00034-0}, journal = {Journal of Biomedical Informatics}, keywords = {ann, classification, logistic, model}, month = {October}, number = {5-6}, pages = {352--359}, posted-at = {2009-01-20 00:59:14}, priority = {2}, title = {Logistic regression and artificial neural network classification models: a methodology review}, url = {http://dx.doi.org/10.1016/S1532-0464(03)00034-0}, volume = {35}, year = {2002} }

TY - JOUR ID - citeulike:3048420 L3 - citeulike-article-id:3048420 N2 - 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. IS - 5-6 JF - Journal of Biomedical Informatics EP - 359 TI - Logistic regression and artificial neural network classification models: a methodology review VL - 35 SP - 352 KW - ann KW - classification KW - logistic KW - model AU - Dreiseitl, Stephan AU - Ohno-Machado, Lucila PY - 2002/10// UR - http://dx.doi.org/10.1016/S1532-0464(03)00034-0 ER -