A Hybrid Brain Stroke Prediction Framework: Integrating Feature Selection, Classification, and Hyperparameter Optimization
Mohammad Amin, Khalid M.O. Nahar, Hasan Gharaibeh, Rabia Emhamed Al Mamlook, Ahmad Nasayreh, Nesrine Atitallah, Ali Gharaibeh, Raneem Hamad, Raed Abu Zitar, Aseel Smerat, Laith Abualigah
Abstract
ABSTRACT Stroke is a leading cause of death and disability worldwide, requiring accurate and early prediction to ensure timely medical intervention. This study proposes a hybrid system that combines optimal feature selection and advanced classification techniques to improve stroke prediction performance. We used a publicly available Harvard Stroke Prediction Data Warehouse dataset, applying multiple feature selection methods: ANOVA, chi‐square, mutual information classification, and analysis of variance to identify relevant features. Five classifiers were examined: Random Forest (RF), K‐Nearest Neighbors (KNN), Decision Tree (DT), XGBoost, and Multilayer Perceptron (MLP). MLP was also used for feature selection through its internal representation learning capabilities. The parameters were fine‐tuned using GridSearchCV. The most effective configuration used selected features from a RF with MLP as a classifier, achieving 99.86% accuracy, 1.00% recall, 99.73% precision, and an F1 score of 99.86%. Compared with existing state‐of‐the‐art models, our proposed system demonstrates superior performance. This approach enables earlier and more accurate stroke detection, supporting healthcare providers in delivering personalized and proactive care to at‐risk individuals.