Analysis of Various Machine Learning Model Performances for Epilepsy Prediction

The paper presents a comprehensive comparative framework demonstrating the critical impact of feature engineering (specifically chi-square and p-value statistical testing) on classical machine learning classifiers for clinical epilepsy type prediction. By bridging raw clinical patient attributes with proper data normalization, the study successfully elevates Support Vector Machine (SVM) accuracy from a baseline of 47% up to 80%, matching the overall top-performing Random Forest and Logistic Regression models at 81% accuracy