Classification of smoking cessation status with a backpropagation neural network

Abstract

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.