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Computational intelligence techniques for efficient delivery of healthcare

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Abstract

Computational intelligence innovation and the use of computers have changed the entire healthcare delivery system. Nurses are the leading crew of healthcare organization. But, these nurses are either lacking in computer usage or automated analysis generated by computers. Therefore, it motivates to study the use of computers and information technology by nurses in Indian healthcare system. Further, it is essential to identify the chief factors where these nurses are lacking while using computers and information technology. This will help the management to take necessary measure to train them and make the healthcare industry more productive in perception with usage of computer and information technology. To this end, data has collected from nurses in hospitals in the state of Tamilnadu, India. Data collection is not beneficial unless it is analyzed and meaningful information obtained from it. In this paper, we hybridize rough set and formal concept analysis to arrive at chief factors affecting the decisions. Rough set is used to analyze the data and to generate rules. These generated rules further passed into formal concept analysis to identify the chief characteristics affecting the decisions. This in turn help the organization to provide adequate training to the nurses and the healthcare system will move further to the next stage.

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References

  1. Cheeseman PC, Self M, Kelly J, Taylor W, Freeman D, Stutz JC. Bayesian classification. AAAI; 1988. p. 607–611.

  2. Lindley DV. Regression and correlation analysis. Time Series and Statistics. London: Palgrave Macmillan; 1990. p. 237– 243.

  3. Molodtsov D. Soft set theory-first results. Comput Math Appl 1999;37(4-5):19–31.

    Article  MathSciNet  Google Scholar 

  4. Zadeh LA. Fuzzy sets. Inf control 1965;8(3):338–353.

    Article  Google Scholar 

  5. Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets Syst 1986;20(1):87–96.

    Article  Google Scholar 

  6. Dubois D, Prade H. Twofold fuzzy sets and rough sets-Some issues in knowledge representation. Fuzzy Sets Syst 1987;23(1):3–18.

    Article  MathSciNet  Google Scholar 

  7. Goguen JA. L-fuzzy sets. J Math Anal Appl 1967;18(1):145–174.

    Article  MathSciNet  Google Scholar 

  8. Pawlak Z, Skowron A. Rough sets and Boolean reasoning. Inf Sci 2007;177(1):41–73.

    Article  MathSciNet  Google Scholar 

  9. Acharjya DP, Tripathy BK. Rough sets on fuzzy approximation spaces and applications to distributed knowledge systems. Int J Artif Intell Soft Comput 2008;1(1):1–14.

    Article  Google Scholar 

  10. Acharjya DP, Tripathy BK. Rough Sets on intuitionistic fuzzy approximation spaces and knowledge representation. Int J Artif Intell Comput Res 2009;1(1):29–36.

    Google Scholar 

  11. Dubois D, Prade H. Rough fuzzy sets and fuzzy rough sets. Int J General Syst 1990;17(2-3):191–209.

    Article  Google Scholar 

  12. Liu G. Rough set theory based on two universal sets and its applications. Knowl-Based Syst 2010;23(2):110–115.

    Article  Google Scholar 

  13. Tripathy BK, Acharjya DP. Approximation of classification and measures of uncertainty in rough set on two universal sets. Int J Adv Sci Technol 2012;40:77–90.

    Google Scholar 

  14. Rathi R, Acharjya DP. A rule based classification for vegetable production using rough set and genetic algorithm. Int J Fuzzy Syst Appl 2018;7(1):74–100.

    Google Scholar 

  15. Rathi R, Acharjya DP. A framework for prediction using rough set and real coded genetic algorithm. Arabian J Sci Eng 2018;43(8):4215–4227.

    Article  Google Scholar 

  16. Anitha A, Acharjya DP. Neural network and rough set hybrid scheme for prediction of missing associations. Int J Bioinf Res Appl 2015;11(6):503–524.

    Article  Google Scholar 

  17. Acharjya DP, Bhattacharjee D. A rough computing based performance evaluation approach for educational institutions. Int J Softw Eng Appl 2013;7(4):331–348.

    Google Scholar 

  18. Anitha A, Acharjya DP. Crop suitability prediction in Vellore District using rough set on fuzzy approximation space and neural network. Neural Comput Appl. 2017:1-18. https://doi.org/10.1007/s00521-017-2948-1.

  19. Greco S, Matarazzo B, Slowinski R. A new rough set approach to evaluation of bankruptcy risk. Operational tools in the management of financial risks. 1998:121–136.

  20. Slowinski R, Zopounidis C. Application of the rough set approach to evaluation of bankruptcy risk. Intell Syst Acc Finance Manage 1995;4(1):27–41.

    Article  Google Scholar 

  21. Dimitras AI, Slowinski R, Susmaga R, Zopounidis C. Business failure prediction using rough sets. Eur J Oper Res 1999;114(2):263–280.

    Article  Google Scholar 

  22. Pawlak Z. Rough sets. Int J Parallel Prog 1982;11(5):341–356.

    MATH  Google Scholar 

  23. Pawlak Z. Rough sets: theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers; 1991.

    Book  Google Scholar 

  24. Pawlak Z, Skowron A. Rough sets: some extensions. Inf Sci 2007;177(1):28–40.

    Article  MathSciNet  Google Scholar 

  25. Saleem Durai MA, Acharjya DP, Kannan A, Sriman Narayana Iyengar NC. An intelligent knowledge mining model for kidney cancer using rough set theory. Int J Bioinf Res Appl 2012;8(5-6):417–435.

    Article  Google Scholar 

  26. Wille R. Formal concept analysis as mathematical theory of concepts and concept hierarchies. Formal concept analysis. Berlin: Springer; 2005. p. 1–33.

  27. Chang LY, Wang GY, Wu Y. An approach for attribute reduction and rule generation based on rough set theory. J Softw 1999;10(11):177–194.

    Google Scholar 

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Authors and Affiliations

  1. School of Information Technology and Engineering VIT, Vellore, India

    Brijendra Singh

  2. School of Computer Science and Engineering VIT, Vellore, India

    D. P. Acharjya

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  1. Brijendra Singh
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  2. D. P. Acharjya
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Correspondence to D. P. Acharjya.

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First Author declares that he has no conflict of interest. Second Author declares that he has no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

This article is part of the Topical Collection on Internet Of Medical Things In E-Health

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Singh, B., Acharjya, D.P. Computational intelligence techniques for efficient delivery of healthcare. Health Technol. 10, 167–185 (2020). https://doi.org/10.1007/s12553-018-00280-6

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  • DOI: https://doi.org/10.1007/s12553-018-00280-6

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