Discovering meaningful information from large amounts of environment and health data to reduce uncertainties in formulating environmental policies.
This study uses knowledge discovery concepts to analyze large amounts of data step by step for the purpose of assisting in the formulation of environmental policy. We performed data cleansing and extracting from existing nation-wide databases, and used regression and classification techniques to analyze the data. The current water hardness in Kaohsiung, Taiwan contributes to the prevention of cardiovascular disease (CVD) but exacerbates the development of renal stones (RS). However, to focus on water hardness alone to control RS would not be cost effective at all, because the existing database parameters do not adequately allow for a clear understanding of RS. Analysis of huge amounts of data can most often turn up the most reliable and convincing results and the use of existing databases can be cost-effective.
Lee
I-Nong
IN
Faculty of Medical Information Management, Kaohsiung Medical University, Kaohsiung City, 807, Taiwan, ROC.
Chang
Wen-Chung
WC
Hong
Yu-Jue
YJ
Liao
Shang-Chih
SC
D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D003710:Demography; D004777:Environment; D005618:Fresh Water / Q000737:chemistry; D048088:Informatics / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D007341:Insurance; D013624:Taiwan