A retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute gastroenteritis in children
The machine learning models can contribute to predicting symptom-based diagnosis of rotavirus-associated acute gastroenteritis in children, especially in resource-limited settings. Further validation of the models using a large dataset is needed for predicting pediatric diarrheic populations with optimum sensitivity and specificity.
Authors
- Chongtham Shyamsunder Singh
- Sourav Paul (ORCID: https://orcid.org/0000-0002-1801-8546)
- Arifullah Mohammed
- Nima D. Namsa (ORCID: https://orcid.org/0000-0002-9412-8915)
- Kasturi Saikia
- Anutee Dolley
- Ghazala Muteeb (ORCID: https://orcid.org/0000-0002-3328-3559)
- Rosy Sarmah (ORCID: https://orcid.org/0000-0003-2564-426X)
- Minhazur Rahman (ORCID: https://orcid.org/0000-0002-3388-4557)
Institutions
- Tezpur University (IN)
- National Institute of Technology Durgapur (IN)
- Universiti Malaysia Kelantan (MY)
- Regional Institute of Medical Sciences (IN)
- King Faisal University (SA)
Publication Details
- Journal
- PeerJ
- Published
- 2025-04-14
- DOI
- https://doi.org/10.7717/peerj.19025
- Citations
- 1
- Primary Topic
- Viral gastroenteritis research and epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 1.40