SGO-Enhanced Random Forest and XGBoost Framework for Accurate Heart Disease Prediction

This study introduces a novel machine learning framework that integrates Social Group Optimization (SGO) with Random Forest and Extreme Gradient Boosting classifiers to enhance heart disease prediction accuracy. By optimizing hyperparameters using SGO, the models achieved up to 97.62% accuracy, improving early diagnosis reliability. Healthcare professionals and patients stand to benefit from more precise risk assessment, potentially reducing cardiovascular mortality worldwide.