Using rough set theory to identify villages affected by birth defects: the example of Heshun, Shanxi, China
Using rough set theory to identify villages affected by birth defects: the example of Heshun, Shanxi, China
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DOI：
10.1080/13658810902960079
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发表时间：
2010-01-01
影响因子：
5.7
通讯作者：
Liao, Yi Lan
中科院分区：
文献类型：
--
作者：
Bai, Hexiang;Ge, Yong;Liao, Yi Lan
本文利用粗糙集理论探索神经管出生缺陷的空间决策规则，寻找与神经管出生缺陷相关的新的空间因素。整个规则归纳过程包括数据转换、属性约简搜索、规则生成、预测或分类、精度评估。我们以神经管先天缺陷普遍存在的和顺为例，验证了该方法的有效性。以和顺地区约50%的村庄作为样本数据，从中提取出所有的规则。同时，以其他村落作为参考数据。然后将从训练数据中提取的规则应用于参考数据。结果表明，规则具有较好的泛化能力。此外，还发现了空间属性与神经管出生缺陷之间的新关系。也就是说，位于该地区第9分水岭的村庄，以及与16度至25度之间的坡度有关的村庄，很容易出现神经管出生缺陷。这一结果为预测神经管出生缺陷率高的地方铺平了道路，并可作为寻找这种疾病的直接原因的初步步骤。
This article uses rough set theory to explore spatial decision rules in neural-tube birth defects and searches for novel spatial factors related to the disease. The whole rule induction process includes data transformation, searching for attribute reducts, rule generation, prediction or classification, and accuracy assessment. We use Heshun as an example, where neural-tube birth defects are prevalent, to validate the approach. About 50% of the villages in Heshun are used as the sample data, from which all of the rules are extracted. Meanwhile, the other villages are used as reference data. The rules extracted from the training data are then applied to the reference data. The result shows that the rules' generalization is reasonably good. Moreover, a novel relationship between the spatial attributes and the neural-tube birth defects was discovered. That is, the villages that lie in Watershed 9 of this district and that are also associated with a gradient of between 16 degrees and 25 degrees are vulnerable to neural-tube birth defects. This result paves the road for predicting where high rates of neural-tube birth defects will occur and can be used as a preliminary step in finding a direct cause for the disease.