A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children
The interpretable CatBoost model can help physicians accurately identify school-aged children with SMPP. This early identification facilitates better treatment options and timely prevention of complications. Furthermore, the SHAP algorithm enhances the model's transparency and increases its trustworthiness in practical applications.
Authors
- Zhiling Zhang (ORCID: https://orcid.org/0009-0005-0020-768X)
- Zhenpeng Gao (ORCID: https://orcid.org/0000-0001-9546-186X)
- Yingying Ye (ORCID: https://orcid.org/0000-0003-0056-030X)
- Weifang Zhou (ORCID: https://orcid.org/0000-0001-6088-4977)
- Chu Chu (ORCID: https://orcid.org/0000-0003-1577-5565)
- Jianlong Chen (ORCID: https://orcid.org/0000-0002-6798-488X)
Institutions
- Soochow University (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1186/s12879-025-10958-8
- Citations
- 7
- Primary Topic
- Pneumonia and Respiratory Infections
- Type
- article
- Field-Weighted Citation Impact
- 9.48
Funders
- Soochow University