A study on dengue fever in bangladesh: Predicting the probability of dengue infection with external behavior with machine learning

Md Sanzidul Islam, Sharun Akter Khushbu, Akm Shahariar Azad Rabby, Touhid Bhuiyan

Research output: Contribution to conferenceConference paper

2 Citations (Scopus)

Abstract

The '2019 Dengue Outbreak' was a nationwide pandemic situation in Bangladesh, particularly in Dhaka city. About 179 people died and 101, 354 confirmed dengue cases were found all over the country. The developing countries like Bangladesh have some limitations in the medical sector and many people don't get proper treatment in time. Henceforth, this research work has attempted to predict the chances to get infected with dengue fever from some external behaviors, like-fever, pain, sitophobia, headache etc. This article has demonstrated a model to predict the probability of dengue fever before taking the pathological test. So, the suspective patient may get some initial diagnosis by giving their anatomical symptoms as input and further this will decrease the dependency on the pathological test for acquiring the primary treatment. Different machine learning models are used to predict the probability and an accuracy near to 100% has been achieved finally.
Original languageEnglish
Pages1717-1721
Number of pages5
DOIs
Publication statusPublished - 6 May 2021
Externally publishedYes
EventProceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021 -
Duration: 6 May 2021 → …

Conference

ConferenceProceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021
Period6/05/21 → …

Keywords

  • Anatomical symptoms
  • Dengue disease
  • Machine Learning model

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