open access

Dengue Diseases Prediction Using SMO Classification

  • Nithya K Assistant Professor, Department of Computer Science and Engineering, Nandha College of Technology, Erode – 638 052, Tamilnadu, India.
  • Anandhasre R Student, Department of Computer Science and Engineering, Nandha College of Technology, Erode – 638 052, Tamilnadu, India.
  • Anusuya D Student, Department of Computer Science and Engineering, Nandha College of Technology, Erode – 638 052, Tamilnadu, India.
  • Moogabigai B Student, Department of Computer Science and Engineering, Nandha College of Technology, Erode – 638 052, Tamilnadu, India.
  • Reshma K.R Student, Department of Computer Science and Engineering, Nandha College of Technology, Erode – 638 052, Tamilnadu, India.

Abstract

The point of this work is to analyze the execution of various grouping procedures. A dengue malady can make extreme harms the general public. Consequently, it is basic to foresee a dengue malady ahead of time to minimalize the harm and misfortune brought about by the ailment. The clinical records kept up are a pool of data with respect to the tainted patients. By keeping this voluminous information we can anticipate the future events of the infection prior and safe gatekeeper the general population. Dengue the worldwide issue is basic in excess of 110 nations. Dengue contamination has jeopardized 2.5 billion populaces all around the globe. Consistently there are 50 million individuals who experience the ill effects of it all around. Dengue fever is a vector borne sickness brought about by the female Aedes Egyptian and Aides Albopictus mosquitoes which adjust well to human conditions. Information mining is a notable system utilized by wellbeing associations for order and forecast of infections.

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