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DOI: 10.14569/IJACSA.2018.090807
PDF

Review of Prediction of Disease Trends using Big Data Analytics

Author 1: Diellza Nagavci
Author 2: Mentor Hamiti
Author 3: Besnik Selimi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 8, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Big Data technologies promise to have a transformative impact in healthcare, public health, and medical research, among other application areas. Several intelligent machine learning techniques were designed and used to provide big data predictive analytics solutions for different illness. Nevertheless, there is no published research for prediction of allergy and respiratory system diseases. However, the impact of research and the finding of different cases is conducive to progress and further development of this. One of the goals of this paper is to devise a systematic mapping study, to explore and analyze existing research about disease prediction in healthcare information. According to the realized investigation of published research from 2012 up to today, we are focusing our research on studies that have been published around big data analytics. With this high number of secondary studies, it is important to conduct a review and provide an overview of the research situation and current developments in this area.

Keywords: Big data; algorithms; data analytics; healthcare; disease prediction; data mining

Diellza Nagavci, Mentor Hamiti and Besnik Selimi, “Review of Prediction of Disease Trends using Big Data Analytics” International Journal of Advanced Computer Science and Applications(IJACSA), 9(8), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090807

@article{Nagavci2018,
title = {Review of Prediction of Disease Trends using Big Data Analytics},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090807},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090807},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {8},
author = {Diellza Nagavci and Mentor Hamiti and Besnik Selimi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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