Bulgiba A M, Fisher M H
Department of Social and Preventive Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.
Health Informatics J. 2006 Sep;12(3):213-25. doi: 10.1177/1460458206066665.
The study investigated the effect of different input selections on the performance of artificial neural networks in screening for acute myocardial infarction (AMI) in Malaysian patients complaining of chest pain. We used hospital data to create neural networks with four input selections and used these to diagnose AMI. A 10-fold cross-validation and committee approach was used. All the neural networks using various input selections outperformed a multiple logistic regression model, although the difference was not statistically significant. The neural networks achieved an area under the ROC curve of 0.792 using nine inputs, whereas multiple logistic regression achieved 0.739 using 64 inputs. Sensitivity levels of over 90 per cent were achieved using low output threshold levels. Specificity levels of over 90 per cent were achieved using threshold levels of 0.4-0.5. Thus neural networks can perform as well as multiple logistic regression models even when using far fewer inputs.
该研究调查了不同输入选择对人工神经网络筛查马来西亚胸痛患者急性心肌梗死（AMI）性能的影响。我们使用医院数据创建了具有四种输入选择的神经网络，并使用这些网络诊断AMI。采用了10倍交叉验证和委员会方法。尽管差异无统计学意义，但所有使用各种输入选择的神经网络均优于多元逻辑回归模型。使用九个输入的神经网络在ROC曲线下的面积为0.792，而使用64个输入的多元逻辑回归为0.739。使用低输出阈值水平可实现超过90%的灵敏度水平。使用0.4 - 0.5的阈值水平可实现超过90%的特异性水平。因此，即使使用少得多的输入，神经网络的表现也能与多元逻辑回归模型相当。
