An adaptive binary PSO to learn Bayesian classifier for prognostic modeling of metabolic syndrome

Satchidananda Dehuri, Rahul Roy, Sung-Bae Cho

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

6 Citations (Scopus)

Abstract

The metabolic syndrome is a combination of medical disorders that have become a significant problem in Asian countries due to the change in lifestyle and food habits. Thus a prognostic model can help the medical experts in diagnosis of the disease. Learnable Bayesian classifier by Adaptive Binary Particle Swarm Optimization (ABPSO) provides a robust formalism for probabilistic modeling that can be used as a predictive tool in medical domain. In this paper, we adopt an ABPSO for adapting the weights of the learnable Bayesian classifier that provides a maximum prediction accuracy and can exhibit an improved capability of removing spurious or little important attributes and help the medical experts in identifying the basis for the disease. Experiments have been conducted with the dataset obtained in Yonchon Country of Korea, and the proposed model provides better performance than the other models.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication
Pages495-501
Number of pages7
DOIs
Publication statusPublished - 2011 Aug 26
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland
Duration: 2011 Jul 122011 Jul 16

Other

Other13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
CountryIreland
CityDublin
Period11/7/1211/7/16

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'An adaptive binary PSO to learn Bayesian classifier for prognostic modeling of metabolic syndrome'. Together they form a unique fingerprint.

  • Cite this

    Dehuri, S., Roy, R., & Cho, S-B. (2011). An adaptive binary PSO to learn Bayesian classifier for prognostic modeling of metabolic syndrome. In Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication (pp. 495-501) https://doi.org/10.1145/2001858.2002039