Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
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Genetic Algorithms in Search, Optimization and Machine Learning
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Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
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This study examined the ability of a backpropagation neural network (BPNN) classifier to distinguish between current and former smokers in the 2000 National Health Interview Survey (NHIS) sample adult file. The BPNN classifier performance exceeded that of random chance, with asymmetric 95% confidence intervals for A"z (area under receiver operating characteristic curve)=(0.7532, 0.7790). Separation of current and former smokers was imperfect, as illustrated by the receiver operating characteristic (ROC) curve. Additionally, performance did not exceed that of a comparison classifier created using logistic regression. Attribute subset selection identified three novel attributes related to smoking cessation status. This study establishes the ability of backpropagation neural networks to classify a complex health behavior, smoking cessation. It also illustrates the hypothesis-generating capacity of data mining methods when applied to large population-based health survey data. Ultimately, BPNN classifiers of smoking cessation status may be useful in decision support systems for smoking cessation interventions.