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 language | English |
|---|---|
| Pages | 1717-1721 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 6 May 2021 |
| Externally published | Yes |
| Event | Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021 - Duration: 6 May 2021 → … |
Conference
| Conference | Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021 |
|---|---|
| Period | 6/05/21 → … |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Anatomical symptoms
- Dengue disease
- Machine Learning model
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