Title: Mining California vital statistical data

Authors: Du Zhang, Quoc Luan Ha, Meiliu Lu

Addresses: Department of Computer Science, California State University, Sacramento, CA 95819-6021, USA. ' Department of Computer Science, California State University, Sacramento, CA 95819-6021, USA. ' Department of Computer Science, California State University, Sacramento, CA 95819-6021, USA

Abstract: Vital statistics data offer a fertile ground for data mining. In this paper, we discuss the results of a data-mining project on the causes of death aspect of the vital statistics data in the state of California. A data-mining tool called Cubist is used to build predictive models out of two million cases over a nine-year period. The objective of our study is to discover knowledge (trends, correlations or patterns) that may not be gleaned through standard techniques. The generated predictive models allow pertinent state agencies to gain insight into various aspects of the death rates in the state of California, to predict health issues related to the causes of death, to offer an aid to decision – or policy-making process and to provide useful information services to the customers. The results obtained in our study contain valuable new information.

Keywords: vital statistics data; causes of death; death rates; mortality data; data mining; predictive models; Cubist; California.

DOI: 10.1504/IJCAT.2006.011999

International Journal of Computer Applications in Technology, 2006 Vol.27 No.4, pp.281 - 297

Published online: 08 Jan 2007 *

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