Feasibility study of a multi-criteria decision-making based hierarchical model for multi-modality feature and multi-classifier fusion: Applications in medical prognosis prediction
Feasibility study of a multi-criteria decision-making based hierarchical model for multi-modality feature and multi-classifier fusion: Applications in medical prognosis prediction
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DOI：
10.1016/j.inffus.2019.09.001
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发表时间：
2020-03-01
影响因子：
18.6
通讯作者：
Zhou, Linghong
中科院分区：
文献类型：
--
作者：
He, Qiang;Li, Xin;Zhou, Linghong
放射组学通过分析来自临床治疗、医学图像和病理等多方面的数据，在肿瘤分级、诊断和预测预后方面具有很大的前景。然而，在实际的临床环境中，探索一种有效的方法来管理各种临床信息，以及选择合适的分类器进行预测建模，仍然是需要的。在这项研究中，我们提出了一种基于多准则决策（MCDM）的分类器融合（MCF）策略，在MCDM框架内组合不同的分类器。在此基础上，研究了一种层次预测方案（H-MCF），将多模态特征和多分类器可靠地连接起来。10个公共UCI数据集和2个临床数据集用于验证所提出的MCF和H-MCF。实验结果表明，与传统融合策略和其他融合架构相比，H-MCF具有优越的预测性能，从而证明了所提出的H-MCF在融合多模态和不同分类器特征信息方面的可行性。
Radiomics has great prospects in terms of tumour grading, diagnosis and prediction of prognosis by analysing multifaceted data from sources such as clinical treatments, medical images, and pathology. However, exploring an effective way to manage miscellaneous clinical information, as well as to select an appropriate classifier for prediction modelling, is still demanding in a practical clinical context. In this study, we propose a multicriterion decision-making (MCDM) based classifier fusion (MCF) strategy to combine different classifiers within an MCDM framework. A hierarchical predictive scheme (H-MCF) based on the proposed MCF is also investigated to reliably link the multi-modality features and multi-classifiers. Ten public UCI datasets and two clinical datasets were used to validate the proposed MCF and H-MCF. The experimental results showed that H-MCF has superior predictive performance when compared with the traditional fusion strategies and other fusion architectures, thus demonstrating the feasibility of the proposed H-MCF in integrating information from features of diversified modalities and different classifiers.