Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model
Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model
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DOI：
10.1016/j.ins.2004.03.002
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发表时间：
2004-05
期刊：
影响因子：
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通讯作者：
S. Tsumoto
中科院分区：
文献类型：
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作者：
S. Tsumoto
由于规则归纳方法生成的规则的长度对于给定类别之间的区分来说是最短的，因此它们往往会生成对于医学专家来说太短的规则。因此，专家很难从领域知识的角度来解释这些规则。本文仔细研究了专家规则的特点，并介绍了一种生成诊断规则的新方法。所提出的方法侧重于鉴别诊断的层次结构，由以下三个过程组成。首先，从数据库中提取决策属性（给定类别）的表征，并根据表征将类别分为几个广义组。然后，归纳出两种子规则，每个广义组的分类规则和每个组内每个类别的规则。最后，这两个部分针对每个决策属性集成为一个规则。所提出的方法在医学数据库上进行了评估，实验结果表明归纳规则正确地代表了专家的决策过程。
Since rule induction methods generate rules whose lengths are the shortest for discrimination between given classes, they tend to generate rules too short for medical experts. Thus, these rules are difficult for the experts to interpret from the viewpoint of domain knowledge. In this paper, the characteristics of experts' rules are closely examined and a new approach to generate diagnostic rules is introduced. The proposed method focuses on the hierarchical structure of differential diagnosis and consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several generalized groups with respect to the characterization. Then, two kinds of sub-rules, classification rules for each generalized group and rules for each class within each group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts' decision processes.