Sparse logistic regression for diagnosis of liver fibrosis in rat by using SCAD-penalized likelihood.
Sparse logistic regression for diagnosis of liver fibrosis in rat by using SCAD-penalized likelihood.
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DOI：
10.1155/2011/875309
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发表时间：
2011
影响因子：
--
通讯作者：
Liu Y
中科院分区：
文献类型：
--
作者：
Yan FR;Lin JG;Liu Y
本研究的目的是通过建立数学模型，寻找肝纤维化进展与某些血清标志物水平之间的定量关系。我们使用平滑剪切绝对偏差（SCAD）惩罚函数提供稀疏逻辑回归来诊断大鼠肝纤维化。它不仅给出了一个高精度的稀疏解，而且还为用户提供了精确的分类概率和类别信息。在模拟和实验情况下，该方法是可比的逐步线性判别分析（SLDA）和稀疏逻辑回归与最小绝对收缩和选择算子（LASSO）的惩罚，通过使用受试者工作特征（ROC）与baidian bootstrap估计曲线下面积（AUC）的诊断灵敏度为选定的变量。结果表明，新的方法提供了一个良好的相关性血清标志物水平和硫代乙酰胺（TAA）诱导的大鼠肝纤维化。同时，该方法也可用于预测肝硬化的发展。
The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis.
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