Brain Stroke Prediction: A PSO Optimized Stacked Ensemble Machine Learning Approach with SMOTEEN Data Balancing
Year: 2025
Type: preprint
Abstract: Prediction of stroke is a critical challenge in healthcare, where early intervention can significantly reduce risks and improve patient outcomes. Traditional methods often struggle with imbalanced datasets and low prediction accuracy. This paper proposes a novel approach to address these issues by combining PSO-optimized Stacked Ensemble ML Classifiers with SMOTEEN data balancing to predict strokes effectively. The technique optimizes 3 Baseline ML Classifiers(RF, SVM, XGB) using Particle Swarm Optimization (PSO) and Stacked Ensemble approach is applied that significantly improving prediction performance. The proposed methodology achieved an impressive 95.57% accuracy, outperforming conventional models such as SVM, LR, RF, KNN, XGB. Overall, the PSO-optimized stacked ensemble approach outperforms traditional machine learning techniques in stroke prediction, providing a more accurate and robust model for early detection and intervention.
Authors: Achin Jain, Arun Kumar Dubey, Meenakshi Gupta, Sarita Yadav, Arvind Panwar, Roger Atanga, Bernardo Lemos, Saurav Mallik
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4411132459