International Journal of Advancements in Technology
Open Access

Abstract

Brain Stroke Classification Using Ensemble Learning Approaches

Houmem Slimi*, Sabeur Abid

Brain Stroke (BS) is one of leading cause of death among humans. Early stroke symptoms must be recognized in order to forecast stroke and encourage a healthy lifestyle. In this study, Machine Learning (ML) techniques were used to build and evaluate a number of models with the goal of developing a reliable framework for estimating the longterm risk of having a stroke. This study's main objective is to introduce a stacking method, which has proven to perform exceptionally well as evidenced by a variety of metrics, including AUC, precision, recall, F-measure and accuracy. The experimental results demonstrate the stacking classification method's superiority over competing strategies by achieving a remarkable AUC of 98.6%, as well as high F-measure, precision and recall rates of 98.4% each and a 98.5% total accuracy.

Published Date: 2025-04-21; Received Date: 2024-03-20