An interpretable machine learning approach for predicting drug-resistant epilepsy in children with tuberous sclerosis complex.

ABSTRACT:

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 predicto

SUBMITTER: Fu J 

PROVIDER: S-EPMC12358403 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature