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Predicting heart transplantation outcomes through data analytics

机译：通过数据分析预测心脏移植结果

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Predicting the survival of heart transplant patients is an important, yet challenging problem since it plays a crucial role in understanding the matching procedure between a donor and a recipient. Data mining models can be used to effectively analyze and extract novel information from large/complex transplantation datasets. The objective of this study is to predict the 1-, 5-, and 9-year patient's graft survival following a heart transplant surgery via the deployment of analytical models that are based on four powerful classification algorithms (i.e. decision trees, artificial neural networks, support vector machines, and logistic regression). Since the datasets used in this study has a much larger number of survival cases than deaths for 1- and 5-year survival analysis and vice versa for 9-year survival analysis, random under sampling (RUS) and synthetic minority over-sampling (SMOTE) are employed to overcome the data-imbalance problems. The results indicate that logistic regression combined with SMOTE achieves the best classification for the 1-, 5-, and 9-year outcome prediction, with area-under-the-curve (AUC) values of 0.624, 0.676, and 0.838, respectively. By applying sensitivity analysis to the data analytical models, the most important predictors and their associated contribution for the 1-, 5-, and 9-year graft survival of heart transplant patients are identified. By doing so, variables, whose importance changes over time, are differentiated. Not only this proposed hybrid approach gives superior results over the literature but also the models and identification of the variables present important retrospective findings, which can be the basis for a prospective medical study. (C) 2016 Elsevier B.V. All rights reserved.
机译：预测心脏移植患者的生存是一个重要的但具有挑战性的问题，因为它在理解供体和受体之间的匹配过程中起着至关重要的作用。数据挖掘模型可用于从大型/复杂移植数据集中有效地分析和提取新信息。这项研究的目的是通过部署基于四种强大分类算法（例如决策树，人工神经网络，支持向量机和逻辑回归）。由于本研究中使用的数据集比1年和5年生存分析的死亡病例要多得多，而9年生存分析的死亡病例则要多得多，因此随机抽样下（RUS）和人工合成过采样（SMOTE） ）用于克服数据不平衡问题。结果表明，逻辑回归与SMOTE结合可获得1年，5年和9年结果预测的最佳分类，曲线下面积（AUC）值分别为0.624、0.676和0.838。通过将敏感性分析应用于数据分析模型，可以确定最重要的预测因素及其对心脏移植患者1年，5年和9年移植物存活的相关贡献。通过这样做，可以区分其重要性随时间变化的变量。不仅这种提出的混合方法比文献提供了更好的结果，而且变量的模型和识别还提供了重要的回顾性发现，这可以作为前瞻性医学研究的基础。 （C）2016 Elsevier B.V.保留所有权利。

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