Bayesian Network Model for Epidemiological Data

Sagar Baviskar; Akash Kabra; Anand Biyani

Bayesian Network Model for Epidemiological Data

Sagar Baviskar * §

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Akash Kabra

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Anand Biyani

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§ College of Engineering Pune, Maharashtra, India.

Bayesian Network Model for Epidemiological Data

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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.

References

12 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Sagar Baviskar, Akash Kabra, Anand Biyani. 2013. "Bayesian Network Model for Epidemiological Data". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 13 (GJCST Volume 13 Issue D2).

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GJCST Volume 13 Issue D2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

bayesian network Data mining epidemiological data health risk model implementation of bayesian network in R

Classification

GJCST-D Classification C.2.1

Submission ReceivedApril 10, 2013
Peer Review Double Blind
Handling Editor
Accepted May 1, 2013
Published May 15, 2013

Version of record

v1.2

Issue date

May 19, 2013

Language

English

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Article Matrices

Total Score: 144

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Sagar Baviskar, Dinesh Lokhande, Anand Biyani, Akash Kabra (PhD/Dr. count: 0)

View Count (all-time): 445

Total Views (Real + Logic): 3369

Total Downloads (simulated): 161

Publish Date: 2013 05, Wed

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Total Views: 3.4K

Total Downloads: 161

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