National Pilot School of Software, Yunnan University, No. 2, Cuihu North Rd., Kunming 650091, China; Queens' College, University of Cambridge, Cambridge CB3 9ET, UK.
National Pilot School of Software, Yunnan University, No. 2, Cuihu North Rd., Kunming 650091, China.
J Biomed Inform. 2018 Feb;78:144-155. doi: 10.1016/j.jbi.2017.11.005. Epub 2017 Nov 11.
From the perspective of clinical decision-making in a Medical IoT-based healthcare system, achieving effective and efficient analysis of long-term health data for supporting wise clinical decision-making is an extremely important objective, but determining how to effectively deal with the multi-dimensionality and high volume of generated data obtained from Medical IoT-based healthcare systems is an issue of increasing importance in IoT healthcare data exploration and management. A novel classifier or predicator equipped with a good feature selection function contributes effectively to classification and prediction performance. This paper proposes a novel bagging C4.5 algorithm based on wrapper feature selection, for the purpose of supporting wise clinical decision-making in the medical and healthcare fields. In particular, the new proposed sampling method, S-C4.5-SMOTE, is not only able to overcome the problem of data distortion, but also improves overall system performance because its mechanism aims at effectively reducing the data size without distortion, by keeping datasets balanced and technically smooth. This achievement directly supports the Wrapper method of effective feature selection without the need to consider the problem of huge amounts of data; this is a novel innovation in this work.
从基于医疗物联网的医疗保健系统中的临床决策角度来看，实现对长期健康数据的有效和高效分析，以支持明智的临床决策，是一个极其重要的目标，但确定如何有效地处理从基于医疗物联网的医疗保健系统中获得的多维和大量生成数据，是物联网医疗保健数据探索和管理中日益重要的问题。具有良好特征选择功能的新型分类器或预测器有助于提高分类和预测性能。本文提出了一种基于包装特征选择的新型装袋 C4.5 算法，旨在支持医疗和保健领域的明智临床决策。特别是，新提出的抽样方法 S-C4.5-SMOTE，不仅能够克服数据扭曲的问题，而且还能通过保持数据集平衡和技术平滑来有效地减少数据量，从而提高整体系统性能。这一成果直接支持了有效的特征选择的包装方法，而无需考虑大量数据的问题；这是这项工作的一项新创新。
