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首页> 外文期刊>BMC Medical Informatics and Decision Making >Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods
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Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods

机译：肾移植后移植物功能延迟的预测：逻辑回归与机器学习方法的比较

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Background Predictive models for delayed graft function (DGF) after kidney transplantation are usually developed using logistic regression. We want to evaluate the value of machine learning methods in the prediction of DGF. Methods 497 kidney transplantations from deceased donors at the Ghent University Hospital between 2005 and 2011 are included. A feature elimination procedure is applied to determine the optimal number of features, resulting in 20 selected parameters (24 parameters after conversion to indicator parameters) out of 55 retrospectively collected parameters. Subsequently, 9 distinct types of predictive models are fitted using the reduced data set: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs; using linear, radial basis function and polynomial kernels), decision tree (DT), random forest (RF), and stochastic gradient boosting (SGB). Performance of the models is assessed by computing sensitivity, positive predictive values and area under the receiver operating characteristic curve (AUROC) after 10-fold stratified cross-validation. AUROCs of the models are pairwise compared using Wilcoxon signed-rank test. Results The observed incidence of DGF is 12.5?%. DT is not able to discriminate between recipients with and without DGF (AUROC of 52.5?%) and is inferior to the other methods. SGB, RF and polynomial SVM are mainly able to identify recipients without DGF (AUROC of 77.2, 73.9 and 79.8?%, respectively) and only outperform DT. LDA, QDA, radial SVM and LR also have the ability to identify recipients with DGF, resulting in higher discriminative capacity (AUROC of 82.2, 79.6, 83.3 and 81.7?%, respectively), which outperforms DT and RF. Linear SVM has the highest discriminative capacity (AUROC of 84.3?%), outperforming each method, except for radial SVM, polynomial SVM and LDA. However, it is the only method superior to LR. Conclusions The discriminative capacities of LDA, linear SVM, radial SVM and LR are the only ones above 80?%. None of the pairwise AUROC comparisons between these models is statistically significant, except linear SVM outperforming LR. Additionally, the sensitivity of linear SVM to identify recipients with DGF is amongst the three highest of all models. Due to both reasons, the authors believe that linear SVM is most appropriate to predict DGF.
机译：背景技术肾移植后延迟移植物功能（DGF）的预测模型通常是使用逻辑回归建立的。我们想评估机器学习方法在DGF预测中的价值。方法包括2005年至2011年间在根特大学医院进行的497例死者捐赠者的肾脏移植。应用特征消除程序来确定最佳特征数，从而从55个回顾性收集的参数中得出20个选定参数（转换为指示符参数后为24个参数）。随后，使用精简数据集拟合了9种不同类型的预测模型：逻辑回归（LR），线性判别分析（LDA），二次判别分析（QDA），支持向量机（SVM）;使用线性，径向基函数和多项式内核），决策树（DT），随机森林（RF）和随机梯度增强（SGB）。通过计算灵敏度，正预测值和10倍分层交叉验证后接收器工作特征曲线（AUROC）下的面积来评估模型的性能。使用Wilcoxon符号秩检验对模型的AUROC进行成对比较。结果观察到的DGF发生率为12.5％。 DT无法区分有和没有DGF（AUROC为52.5％）的接受者，并且不如其他方法。 SGB，RF和多项式SVM主要能够识别没有DGF（AUROC分别为77.2、73.9和79.8％）的接收者，并且仅胜过DT。 LDA，QDA，径向SVM和LR也具有识别DGF受体的能力，从而具有更高的区分能力（AUROC分别为82.2％，79.6％，83.3％和81.7％），胜过DT和RF。线性SVM的判别能力最高（AUROC为84.3％），优于径向SVM，多项式SVM和LDA的每种方法。但是，它是唯一优于LR的方法。结论仅有LDA，线性SVM，径向SVM和LR的判别能力高于80％。这些模型之间的成对AUROC比较均无统计学意义，只有线性SVM表现优于LR。此外，线性SVM识别DGF受体的敏感性在所有模型中均位居前三位。由于这两个原因，作者认为线性SVM最适合预测DGF。

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