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, Volume 77, Issue 17, pp 21995–22006 | Cite as

Context-based probability neural network classifiers realized by genetic optimization for medical decision making

  • Dan Wang
  • Shaohua Wan
  • Nadra Guizani
Article
  • 92 Downloads

Abstract

In this paper, we proposed context-based probability neural network (CPNN) classifiers for solving a medical decision making problems. The concept of “contexts” coming from the clustering research area is explored here to construct the second layer of probability neural network classifiers. Furthermore, genetic algorithm is used to optimize the structure parameters when designing the proposed CPNN. In contrast to the known probability neural networks, the proposed CPNN archive a better accuracy classification rate. Several known data sets are utilized to evaluate the performance of CPNN. Experimental results demonstrate that the relationship between the selected features and disease are more apparent in comparison with the conventional neural network models.

Keywords

Medical decision making Extraction of features Probabilistic neural network (PNN) Genetic algorithm 

Notes

Acknowledgements

This work was supported by the Foundation of Educational Commission of Tianjin City, China (Grant No. 20140803), supported by the Innovation Foundation for Young Teachers of Tianjin University of Science and Technology, China (Grant No. 2014CXLG30).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and Information EngineeringTianjin University of Science & TechnologyTianjinChina
  2. 2.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  3. 3.School of Electrical & Computer EngineeringPurdue UniversityWest LafayetteUSA

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