AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction
AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction
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DOI：
10.1016/j.eswa.2007.04.015
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发表时间：
2008-05-04
影响因子：
8.5
通讯作者：
Zhang, Byoung-Tak
中科院分区：
文献类型：
--
作者：
Eom, Jae-Hong;Kim, Sung-Chun;Zhang, Byoung-Tak
传统的临床决策支持系统通常基于单个分类器或这些模型的简单组合，表现出中等的性能。在本文中，我们提出了一种基于分类器集成的方法来支持基于适配体芯片的心血管疾病（CVD）的诊断。这种AptaCDSS-E系统通过利用不同分类器的集合克服了传统的性能限制。最近的调查显示，心血管疾病是死亡的主要原因之一，如果能够进行精确的诊断，可以节省大量的生命。对于CVD诊断，我们的系统将一组四个不同的分类器与集成相结合。采用支持向量机和神经网络作为基分类器。决策树和贝叶斯网络也被用来增强系统。使用四个基于适体的生物芯片数据集（包括含有66个样品的CVD数据）来训练和测试系统。另外三个补充数据集用于缓解数据不足的问题。我们通过与基于单个分类器的模型的结果进行比较，研究了基于集成的系统与几种不同的聚合方法的有效性。AptaCDSS-E系统的预测性能用交叉验证检验进行评估。实验结果表明，该系统具有较高的诊断准确率（> 94%）和较小的预测差异区间（< 6%），证明了其在疾病诊断的临床决策过程中的实用性。此外，还发现了10种可能的生物标志物以供进一步研究。(c)2007爱思唯尔有限公司保留所有权利。
Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (> 94%) and comparably small prediction difference intervals (< 6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation. (c) 2007 Elsevier Ltd. All rights reserved.