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Graft survival prediction in liver transplantation using artificial neural network models

机译:人工神经网络模型预测肝移植的存活率

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The use of computer based learning models in medical domain has become a significant area of research. Organ transplantation is one of the main areas where prognosis models are being used for predicting the survival of patients. Post transplantation mortality rate is reduced if there exists an intelligent system that can pick out the correct donor-recipients pairs from a pool of donor and recipient data. In this paper, we propose a survival prediction model to define three month mortality of patients after Liver Transplantation. We used an Artificial Neural Network model for the survival rate of Liver Transplantation. The data for the study was gathered from United Network for Organ Sharing transplant registry. The main objective of the study is to develop a model for short-term survival prediction of liver patients. With 10-fold cross validation we were divided the whole data into training and test data which gives an accuracy of 99.74% by Multilayer Perceptron Artificial Neural Network model. We also compared the model with other classification models using various error performance measures. To ensure accuracy we experimented our model with existing models and proved the result. (C) 2016 Elsevier B.V. All rights reserved.
机译:在医学领域中基于计算机的学习模型的使用已经成为重要的研究领域。器官移植是使用预后模型预测患者存活率的主要领域之一。如果存在一个智能系统,该系统可以从供体和受体数据集中选择正确的供体-受体对,则可以降低移植后的死亡率。在本文中,我们提出了生存预测模型来定义肝移植后患者三个月的死亡率。我们使用人工神经网络模型来评估肝移植的存活率。该研究的数据是从美国器官共享器官移植联合注册网络收集的。该研究的主要目的是为肝病患者的短期生存预测建立模型。通过10倍交叉验证,我们通过多层感知器人工神经网络模型将整个数据分为训练数据和测试数据,其准确性为99.74%。我们还将模型与使用各种错误性能指标的其他分类模型进行了比较。为了确保准确性,我们使用现有模型对模型进行了实验并证明了结果。 (C)2016 Elsevier B.V.保留所有权利。

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