A serial and parallel genetic based learning algorithm for Bayesian classifier to predict metabolic syndrome

S. Dehuri, B. S.P. Mishra, R. Roy, S. B. Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a serial and parallel genetic based learnable bayesian classifier for designing a prognostic model for metabolic syndrome. The objective of the classifier is to address the fundamental problem of finding the optimal weight in the learnable bayesian classifier, by serial GA, and minimize the response time by parallel GA. The algorithms exhibit an improved capability to eliminate spurious features from the large dataset and aid the researchers in identifying those features that are solely responsible for high prediction accuracy. The effectiveness of the classifier are demonstrated using metabolic syndrome dataset obtained from Yonchon County of Korea.

Original languageEnglish
Title of host publicationCompute 2011 - 4th Annual ACM Bangalore Conference
DOIs
Publication statusPublished - 2011 Jun 9
Event4th Annual ACM Bangalore Conference, Compute 2011 - Bangalore, India
Duration: 2011 Mar 252011 Mar 26

Publication series

NameCompute 2011 - 4th Annual ACM Bangalore Conference

Other

Other4th Annual ACM Bangalore Conference, Compute 2011
CountryIndia
CityBangalore
Period11/3/2511/3/26

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All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications

Cite this

Dehuri, S., Mishra, B. S. P., Roy, R., & Cho, S. B. (2011). A serial and parallel genetic based learning algorithm for Bayesian classifier to predict metabolic syndrome. In Compute 2011 - 4th Annual ACM Bangalore Conference [1] (Compute 2011 - 4th Annual ACM Bangalore Conference). https://doi.org/10.1145/1980422.1980423