Using rough set theory to identify villages affected by birth defects: the example of Heshun, Shanxi, China

Abstract

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° and 25° 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.

Keywords:

• rough set
• neural-tube birth defects
• spatial analysis

Acknowledgments

This work was supported is part by the National 973 Program 2006CB701305, by the National Natural Science Foundation of China under Grant 40671136 and 40471111, and by the Ministry of Science and Technology of the People's Republic of China 2007DFC20180.