Multiclass Benchmarking Framework for Automated Acute Leukaemia Detection and Classification Based on BWM and Group-VIKOR
Multiclass Benchmarking Framework for Automated Acute Leukaemia Detection and Classification Based on BWM and Group-VIKOR
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DOI：
10.1007/s10916-019-1338-x
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发表时间：
2019-07-01
影响因子：
5.3
通讯作者：
Mohammed, K. I.
中科院分区：
文献类型：
--
作者：
Alsalem, M. A.;Zaidan, A. A.;Mohammed, K. I.
本文旨在协助医疗机构管理部门在选择合适的急性白血病多类分类模型时做出正确的决策。在本文中，我们提出了一个框架，该框架将帮助这些部门对可用的多类分类模型进行评估、基准测试和排名，以选择最佳的模型。医疗组织在这种努力中不断面临评估和基准测试的挑战，特别是在没有单一模型优于的情况下。此外，急性白血病模型多类分类选择不当可能会给医疗机构带来高昂的代价。例如，当一名患者死亡时，此类组织将因模型未能实现其预期结果的事件而受到法律或财务起诉。在评估和基准测试方面，由于多重评估和相互冲突的标准，多类分类模型是具有挑战性的过程。本研究构建了基于 2 组多重评估标准和 22 个多类分类模型交叉的决策矩阵 (DM)。然后使用包含 72 个急性白血病样本（其中包括 5327 个基因）的数据集对矩阵进行评估。随后，多标准决策（MCDM）技术被用于多类分类模型的基准测试和排名。 MCDM 使用的技术包括集成 BWM 和 VIKOR。 BWM用于评估标准的权重计算，而VIKOR用于对分类模型进行基准和排名。 VIKOR 还被用于两种决策环境：个人和群体决策以及内部和外部群体聚合。结果表明：（1）BWM和VIKOR的集成可以有效解决多类分类模型的基准/选择问题。 (2)内部和外部VIKOR群体决策得到的分类模型的等级几乎相同，基于两者的最佳多类分类模型是贝叶斯。 Naive Byes Updateable”，最糟糕的是 Trees.LMT”。 (3)客观验证中各组得分存在显着性差异，表明内部和外部VIKOR群体决策的排序结果是有效的。
This paper aims to assist the administration departments of medical organisations in making the right decision on selecting a suitable multiclass classification model for acute leukaemia. In this paper, we proposed a framework that will aid these departments in evaluating, benchmarking and ranking available multiclass classification models for the selection of the best one. Medical organisations have continuously faced evaluation and benchmarking challenges in such endeavour, especially when no single model is superior. Moreover, the improper selection of multiclass classification for acute leukaemia model may be costly for medical organisations. For example, when a patient dies, one such organisation will be legally or financially sued for incidents in which the model fails to fulfil its desired outcome. With regard to evaluation and benchmarking, multiclass classification models are challenging processes due to multiple evaluation and conflicting criteria. This study structured a decision matrix (DM) based on the crossover of 2 groups of multi-evaluation criteria and 22 multiclass classification models. The matrix was then evaluated with datasets comprising 72 samples of acute leukaemia, which include 5327 gens. Subsequently, multi-criteria decision-making (MCDM) techniques are used in the benchmarking and ranking of multiclass classification models. The MCDM used techniques that include the integrated BWM and VIKOR. BWM has been applied for the weight calculations of evaluation criteria, whereas VIKOR has been used to benchmark and rank classification models. VIKOR has also been employed in two decision-making contexts: individual and group decision making and internal and external group aggregation. Results showed the following: (1) the integration of BWM and VIKOR is effective at solving the benchmarking/selection problems of multiclass classification models. (2) The ranks of classification models obtained from internal and external VIKOR group decision making were almost the same, and the best multiclass classification model based on the two was Bayes. Naive Byes Updateable' and the worst one was Trees.LMT'. (3) Among the scores of groups in the objective validation, significant differences were identified, which indicated that the ranking results of internal and external VIKOR group decision making were valid.