Fusion of XGBOOST and Random Forest for Accurate Stroke Prediction
1st Md Saif Ali Computer science and engineering Gurunanak institutions technical
campus Ibrahimpatnam,Telangana
mohammadsaifali87@gmail.com
2nd Mamidipally Uday Kiran Computer science and engineering Gurunanak institutions technical
campus Ibrahimpatnam,Telangana
udaykiranmamidipally@gmail.com
3rd Lakkampelly Adithya Computer science and engineering Gurunanak institutions technical
campus Ibrahimpatnam,Telangana adithya4446@gmail.com
4th Ms.Rajashree Sutrawe Computer science and engineering Gurunanak institutions technical
campus Ibrahimpatnam,Telangana raj.sutrawe@gniindia.org
Abstract—Stroke is a life-threatening medical condition caused by disrupted blood flow to the brain, representing a major global health concern with significant health and economic consequences. Researchers are working to tackle this challenge by developing automated stroke prediction algorithms, which can enable timely interventions and potentially save lives. As the global population ages, the risk of stroke increases, making the need for accurate and reliable prediction systems more critical. In this study, we evaluate the performance of an advanced machine learning (ML) approach, focusing on XGBoost and a hybrid model combining XGBoost with Random Forest, by comparing it against six established classifiers. We assess the models based on their generalization ability and prediction accuracy. The results show that more complex models outperform simpler ones, with the best-performing model achieving an accuracy of 96%, while other models range from 84% to 96%. Additionally, the proposed framework integrates both global and local explainability techniques, providing a standardized method for interpreting complex models. This approach enhances the understanding of decision-making processes, which is essential for improving stroke care and treatment. Finally, we suggest expanding the model to a web-based platform for stroke detection, extending its potential impact on public health.
Keywords– Model Fusion, Feature Selection, Predictive Modeling, Supervised Learning, Data Preprocessing
