An interpretable machine learning approach for predicting drug-resistant epilepsy in children with tuberous sclerosis complex
The RF-based prediction model provides a valuable tool for early identification of children with TSC at high risk for DRE, supporting individualized treatment decisions. The integration of SHAP improves model transparency and enhances clinical interpretability.
Authors
- Zhixian Yang (ORCID: https://orcid.org/0000-0002-9649-8725)
- Jiong Qin (ORCID: https://orcid.org/0000-0002-9109-3286)
- Genfu Zhang
- Jiaxin Fu (ORCID: https://orcid.org/0009-0009-9337-1452)
Institutions
- Peking University (CN)
- Peking University People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-08-04
- DOI
- https://doi.org/10.3389/fneur.2025.1623212
- Primary Topic
- Tuberous Sclerosis Complex Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00