Variable selection using the optimal ROC curve: An application to a traditional Chinese medicine study on osteoporosis disease
Variable selection using the optimal ROC curve: An application to a traditional Chinese medicine study on osteoporosis disease
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DOI：
10.1002/sim.3980
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发表时间：
2012-03
影响因子：
2
通讯作者：
Xiaoping Zhou;B. Chen;Y. M. Xie;F. Tian;H. Liu;X. Liang
中科院分区：
文献类型：
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作者：
Xiaoping Zhou;B. Chen;Y. M. Xie;F. Tian;H. Liu;X. Liang
在生物医学研究中，有多种可用的信息来源，其中只有少数与疾病有关。对这些与疾病相关的因素进行选择和组合，对于预测一个新对象的疾病状况具有重要意义。接收工作特征（ROC）技术在疾病分类中得到了广泛的应用，其分类准确率可以用ROC曲线下面积（area under The ROC curve， AUC）来衡量。在本文中，我们将最新的变量选择方法与AUC方法相结合，以优化多种危险因素的诊断准确性。我们首先描述了一种新的和一些最近的基于AUC的方法，可以有效地结合多种危险因素进行疾病分类。然后，我们将其应用于一项新的临床研究的数据分析，探讨中医症状和标准西医危险因素的结合是否可以提高骨质疏松症（OP）诊断的鉴别准确性。综上所述，结合中医症状和西医危险因素，可以更好地诊断原发性OP。版权所有©2011 John Wiley & Sons， Ltd
In biomedical studies, there are multiple sources of information available of which only a small number of them are associated with the diseases. It is of importance to select and combine these factors that are associated with the disease in order to predict the disease status of a new subject. The receiving operating characteristic (ROC) technique has been widely used in disease classification, and the classification accuracy can be measured with area under the ROC curve (AUC). In this article, we combine recent variable selection methods with AUC methods to optimize diagnostic accuracy of multiple risk factors. We first describe one new and some recent AUC‐based methods for effectively combining multiple risk factors for disease classification. We then apply them to analyze the data from a new clinical study, investigating whether a combination of traditional Chinese medicine symptoms and standard Western medicine risk factors can increase discriminative accuracy in diagnosing osteoporosis (OP). Based on the results, we conclude that we can make a better diagnosis of primary OP by combining traditional Chinese medicine symptoms with Western medicine risk factors. Copyright © 2011 John Wiley & Sons, Ltd.