Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications
Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications
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DOI：
10.1504/ijdmb.2006.009919
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发表时间：
2006-06
影响因子：
0.3
通讯作者：
Yuanchen He;Yuchun Tang;Yanqing Zhang;Rajshekhar Sunderraman
中科院分区：
文献类型：
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作者：
Yuanchen He;Yuchun Tang;Yanqing Zhang;Rajshekhar Sunderraman
由于生物医学分类问题的复杂性，不可能建立一个预测准确率100%的完美分类器。因此，更现实的目标是建立有效的决策支持系统（DSS）。这里的“有效”意味着 DSS 不仅应该准确地预测看不见的样本，而且还应该以人类可以理解的方式工作。在本文中，我们提出了一种新颖的自适应模糊关联规则（FAR）挖掘算法，名为 FARM-DS，为生物医学领域的二元分类问题构建这样的 DSS。在训练阶段，执行四个步骤来挖掘 FAR，然后将其用于预测测试阶段中未见过的样本。新的 FARM-DS 算法在两个公开可用的医学数据集上进行评估。实验结果表明FARM-DS在预测精度方面具有竞争力。更重要的是，挖掘的 FAR 由于易于解释，为疾病诊断提供了强有力的决策支持。
Due to complexity of biomedical classification problems, it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). Here 'effective' means that a DSS should not only predict unseen samples accurately, but also work in a human-understandable way. In this paper, we propose a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, to build such a DSS for binary classification problems in the biomedical domain. In the training phase, four steps are executed to mine FARs, which are thereafter used to predict unseen samples in the testing phase. The new FARM-DS algorithm is evaluated on two publicly available medical datasets. The experimental results show that FARM-DS is competitive in terms of prediction accuracy. More importantly, the mined FARs provide strong decision support on disease diagnoses due to their easy interpretability.