Machine learning-based assessment of seizure risk predictors in myelomeningocele patients: A single-center retrospective cohort study.

Background: Myelomeningocele (MMC) is a severe congenital malformation of the CNS (central nervous system) that often leads to seizures due to factors such as shunt complications and hydrocephalus. This study aims to develop a machine learning model to predict the likelihood of seizures in MMC patients by analyzing various predictors. Methods: This retrospective study involved 103 MMC patients. Factors such as demographics, MMC location, shunt history, and imaging were analyzed using the random forest classifier, the support vector classifier, and logistic regression. Model performance was assessed through bootstrap estimates, cross-validation, classification reports, and area under the curve (AUC). Results: Of the evaluated patients, 11 experienced seizures. The key influencing factors included gestational age, sacral location, hydrocephalus, shunt history, and corpus callosum dysgenesis. Machine learning (ML) models predicted seizure risk with an accuracy of 86-92% and an AUC ranging from 0.764 to 0.865. Significant predictors were imaging findings, shunt infection history, and gestational age. Conclusion: ML models effectively predict seizure risk in MMC patients, with certain variables showing strong associations and significant impact.