© 2013. Sagar Baviskar. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-
Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution,
and reproduction inany medium, provided the original work is properly cited.
Global Journal of Computer Science and Technology
Neural & Artificial Intelligence
Volume 13 Issue 2 Version 1.0 Year 2013
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals Inc. (USA)
Online ISSN: 0975-4172 & Print ISSN: 0975-4350
Bayesian Network Model for Epidemiological Data (Radiation
exposure and circulatory disease risk: Hiroshima and Nagasaki
atomic bomb survivor data)
By Sagar Baviskar
College of Engineering Pune, India
Abstract - This documentation describes the implementation of Bayesian Network on Hiroshima Nagasaki
atomic bomb survivor data, using “R” software. Bayesian networks, a state-of-the art representation of
probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern
recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks
allow investigators to combine domain knowledge with statistical data. This tailored discussion presents the
basic concepts of Bayesian networks and its use for building a health risk model on Epidemiological data. The
main objectives of our study is to find interdependencies between various attributes of data and to determine
the threshold value of radiation dosage under which death counts are negligible.
Keywords : bayesian network; data mining; epidemiological data, health risk model, implementation of
bayesian network in R.
GJCST-D Classification : C.2.1
BayesianNetworkModelforEpidemiologicalDataRadiationexposureandcirculatorydiseaseriskHiroshimaandNagasakiatomicbombsurvivordata
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