Predicting seizure outcome after epilepsy surgery: do we need more complex models, larger samples, or better data?

Abstract

AbstractObjectiveThe accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if 1) training more complex models, 2) recruiting larger sample sizes, or 3) using data-driven selection of clinical predictors would improve our ability to predict post-operative seizure outcome. We also conducted the first external validation of a machine learning model trained to predict post-operative seizure outcome.MethodsWe performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a single tertiary center. We extracted patient information from medical records and trained three models – a logistic regression, a multilayer perceptron, and an XGBoost model – to predict one-year post-operative seizure outcome on our dataset. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up toN=2,000. Finally, we examined the impact of predictor selection on model performance.ResultsOur logistic regression achieved an accuracy of 72% (95% CI=68-75%, AUC=0.72), while our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP=67-74%, AUCMLP=0.70; 95% CIXGBoost own=68-75%, AUCXGBoost own=0.70). There was no significant difference in performance between our three models (allP>0.4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI=59-67%, AUC=0.62;PLR=0.005,PMLP=0.01,PXGBoost own=0.01) on our data. All models showed improved performance with increasing sample size, with limited improvements aboveN=400. The best model performance was achieved with data-driven feature selection.SignificanceWe show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict post-operative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.