Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia.
BMC Bioinformatics. 2013 Jun 25;14:206. doi: 10.1186/1471-2105-14-206.
Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using a never before analysed data set from ACT Pathology (Canberra, Australia) relating to HBV and HCV patients.
It was easier to predict immunoassay positive cases than negative cases of HBV or HCV. While applying an ensemble-based approach rather than a single classifier had a small positive effect on the accuracy rate, this also varied depending on the virus under analysis. Finally, scaling data before prediction also has a small positive effect on the accuracy rate for this dataset. A graphical analysis of the distribution of accuracy rates across ensembles supports these findings.
Laboratories looking to include machine learning as part of their decision support processes need to be aware that the infection outcome, the machine learning method used and the virus type interact to affect the enhanced laboratory diagnosis of hepatitis virus infection, as determined by primary immunoassay data in concert with multiple routine pathology laboratory variables. This awareness will lead to the informed use of existing machine learning methods, thus improving the quality of laboratory diagnosis via informatics analyses.
决策树等高级数据挖掘技术已成功用于预测复杂医疗环境中的各种结果。此外，先前的研究表明，将一组单独训练的树的结果组合成基于集成的分类器可以提高整体分类准确性。本文研究了数据预处理、使用袋装构建集成以及简单多数票来组合常规病理实验室数据的分类预测的效果，特别是为了克服乙型肝炎病毒（HBV）和丙型肝炎病毒（HCV）阴性病例与 HBV 或 HCV 免疫测定阳性病例之间的巨大不平衡。这些方法使用来自 ACT 病理学（澳大利亚堪培拉）的从未分析过的 HBV 和 HCV 患者数据集进行了说明。
预测免疫测定阳性病例比 HBV 或 HCV 阴性病例更容易。虽然应用基于集成的方法而不是单个分类器对准确率有很小的积极影响，但这也取决于正在分析的病毒。最后，在预测之前对数据进行缩放也对该数据集的准确率有很小的积极影响。对准确率在集成中的分布的图形分析支持了这些发现。
希望将机器学习作为其决策支持过程一部分的实验室需要意识到，感染结果、使用的机器学习方法和病毒类型相互作用，以影响通过主要免疫测定数据与多个常规病理实验室变量共同确定的肝炎病毒感染的增强实验室诊断。这种意识将导致对现有机器学习方法的明智使用，从而通过信息学分析提高实验室诊断的质量。
