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首页> 外文期刊>Journal of biomedical informatics. >Classification of smoking cessation status with a backpropagation neural network.
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Classification of smoking cessation status with a backpropagation neural network.

机译：用反向传播神经网络对戒烟状态进行分类。

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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.
机译：这项研究检查了反向传播神经网络（BPNN）分类器在2000年国家健康访问调查（NHIS）成人样本文件中区分当前吸烟者和以前吸烟者的能力。 BPNN分类器的性能超过了随机机会的性能，A（z）（接收器工作特性曲线下的面积）的不对称95％置信区间=（0.7532，0.7790）。如吸烟者工作特征（ROC）曲线所示，当前吸烟者和以前吸烟者的分离是不完善的。此外，性能不超过使用逻辑回归创建的比较分类器的性能。属性子集选择确定了三个与戒烟状态有关的新颖属性。这项研究建立了反向传播神经网络对复杂健康行为（戒烟）进行分类的能力。它还说明了将数据挖掘方法应用于基于人口的大型健康调查数据时产生假设的能力。最终，戒烟状态的BPNN分类器可能在戒烟干预措施的决策支持系统中很有用。

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