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Knowledge Discovery in Mortality Records: An Info-Fuzzy Approach
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BibTeX
@MISC{Last_knowledgediscovery,
author = {Mark Last and Oded Maimon and Abraham Kandel and Abraham K},
title = {Knowledge Discovery in Mortality Records: An Info-Fuzzy Approach},
year = {}
}
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Abstract
Introduction Causes of death are a constant subject of medical and demographic research. The issues of interest include the change of the leading causes over time, geographical patterns of deadly diseases, life expectancy, tracking diseases to genetic factors, and many others (Blij and Murphy, 1998). The importance of these studies goes beyond the area of medical 1 Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel research and health care industry. For example, the life insurance companies have to analyze mortality data in determining the terms of their policies. Unfortunately, the tools used in the research on death causality have been so far limited to the standard statistical techniques, like summarization, regression, analysis of variance, etc. In the last decade, more advanced methods of finding patterns in data have become known under the general name of "data mining", and a formal framework for the entire proces
Keyphrases
mortality record info-fuzzy approach knowledge discovery many others demographic research leading cause israel research entire proces last decade data mining introduction cause industrial engineering life expectancy formal framework genetic factor life insurance company health care industry deadly disease advanced method standard statistical technique constant subject tel-aviv university mortality data geographical pattern general name death causality
