Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

Journal: BMC medical informatics and decision making
Published Date: Jun 18, 2015

Abstract

BACKGROUND: Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality.

Authors

  • Anna L Buczak
    Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD, 20723-6099, USA. anna.buczak@jhuapl.edu.
  • Benjamin Baugher
    Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD, 20723-6099, USA.
  • Erhan Guven
    Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD, 20723-6099, USA.
  • Liane C Ramac-Thomas
    Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD, 20723-6099, USA.
  • Yevgeniy Elbert
    Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD, 20723-6099, USA.
  • Steven M Babin
    Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD, 20723-6099, USA.
  • Sheri H Lewis
    Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD, 20723-6099, USA.

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