Combination of binary particle swarm optimization and random forest for stroke disease prediction

Abstract

Stroke is a leading cause of death and disability worldwide, making early risk prediction critical for prevention. Machine learning methods such as random forest (RF) have shown strong predictive performance, but accuracy can be further improved through effective feature selection. This research proposes an integrated model that combines binary particle swarm optimization (BPSO) for feature selection with RF for stroke risk classification. Experiments were conducted on two public datasets: the stroke prediction dataset (SPD) and the brain stroke dataset (BSD). Data preprocessing included handling missing values, normalization, and the synthetic minority oversampling technique (SMOTE) to mitigate the minority and majority classes. BPSO was employed to select the most informative features, followed by RF for classification. The BPSO-RF model delivered superior accuracies of 96.13% on the SPD and 96.07% on the BSD, outperforming competing classifiers and feature selection techniques. Important features such as gender, age, work type, residence type, average glucose level, body mass index (BMI), and smoking status were consistently identified as key predictors. These results indicate that integrating swarm intelligence with ensemble learning can effectively improve stroke risk prediction and support clinical decision-making.