ORIGINAL RESEARCH article
Front. Neurol.
Sec. Pediatric Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1623212
An Interpretable Machine Learning Approach for Predicting Drug-Resistant Epilepsy in Children with Tuberous Sclerosis Complex
Provisionally accepted
- Peking University People's Hospital, Beijing, China
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Background: This study developed and validated an interpretable machine learning (ML) algorithm for predicting the risk of drug-resistant epilepsy (DRE) in children with Tuberous sclerosis (TSC). Methods: To estimate the risk of DRE in pediatric TSC patients, an interpretable ML model was developed and validated. Clinical data were retrospectively collected from 88 pediatric patients with TSC-related epilepsy. 9 ML algorithms were applied, such as random forest (RF), to construct predictive models. To improve interpretability, SHapley Additive exPlanations (SHAP) were employed, providing both global and individualized feature importance explanations.Results: The RF model outperformed all other algorithms, yielding an AUC of 0.862 and a specificity of 0.930. Key predictors of DRE included a history of infantile epileptic spasms syndrome (IESS), multifocal discharges on EEG, three or more cortical tubers, and the use of three or more antiseizure medications (ASMs). The model was further evaluated using tenfold cross-validation and showed good calibration and clinical utility, as confirmed by decision curve analysis (DCA).Conclusion: 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.
Keywords: machine learning, Model interpretability, predictive model, tuberous sclerosis complex, Drug-resistant epilepsy
Received: 05 May 2025; Accepted: 21 Jul 2025.
Copyright: © 2025 Jie, Genfu, Yang and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhixian Yang, Peking University People's Hospital, Beijing, China
Jiong Qin, Peking University People's Hospital, Beijing, China
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