Leveraging Machine Learning for Dengue Prevalence Prediction

Aabid Mushtaq Najar; Arpan Bhattacharya; Shivam Sharma; Bansi Dhar Jha; Hashim Zahoor

doi:10.2991/978-94-6239-768-2_24

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Leveraging Machine Learning for Dengue Prevalence Prediction

Authors

Aabid Mushtaq Najar¹^{, *}, Arpan Bhattacharya², Shivam Sharma³, Bansi Dhar Jha⁴, Hashim Zahoor⁵

¹Assistant Professor, School of Computer Applications, Lovely Professional University, Punjab, India

²Research Scholar, School of Computer Applications, Lovely Professional University, Punjab, India

³Research Scholar, School of Computer Applications, Lovely Professional University, Punjab, India

⁴Research Scholar, School of Computer Applications, Lovely Professional University, Punjab, India

⁵Assistant Professor, School of Computer Applications, Lovely Professional University, Punjab, India

^*Corresponding author. Email: aabidrahie@gmail.com

Corresponding Author

Aabid Mushtaq Najar

Available Online 7 September 2026.

DOI: 10.2991/978-94-6239-768-2_24 How to use a DOI?
Keywords: Google Form; Data Pre-processing; Machine Learning; Hospital
Abstract: Dengue fever is a serious health issue worldwide that is particularly common in warm climates. The diagnosis is based on the identification of symptoms such as fever, headache, joint pain, and rash, which are verified by laboratory testing. Dengue cases are frequently underreported, according to studies that emphasize the need for improved tracking. This study examined dengue hazards and prevalence in the rural and urban areas of India. Hospitals, surveys, and laboratories provided the data. High dengue rates, particularly among young people in metropolitan areas, are associated with characteristics such as high population density and inadequate sanitation. Most illnesses occur during the rainy season. To reduce dengue, focused initiatives such as improved waste management and education are essential. This study will help to improve strategies for preventing and managing dengue fever.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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Volume Title: Proceedings of the Third International Conference on Recent Advances in Computing Sciences (RACS 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 7 September 2026
ISBN: 978-94-6239-768-2
ISSN: 1951-6851
DOI: 10.2991/978-94-6239-768-2_24 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

Cite this article

ris enw bib

TY  - CONF
AU  - Aabid Mushtaq Najar
AU  - Arpan Bhattacharya
AU  - Shivam Sharma
AU  - Bansi Dhar Jha
AU  - Hashim Zahoor
PY  - 2026
DA  - 2026/09/07
TI  - Leveraging Machine Learning for Dengue Prevalence Prediction
BT  - Proceedings of the Third International Conference on Recent Advances in Computing Sciences (RACS 2025)
PB  - Atlantis Press
SP  - 227
EP  - 233
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-768-2_24
DO  - 10.2991/978-94-6239-768-2_24
ID  - Najar2026
ER  -

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