NeuroShield: AI-Powered Brain Stroke Detection Using Advanced Machine Learning Algorithms

Authors- A. Daiva Krupa Nirmala, Pedireddy Harika, Singarapu Aneela Deepthi, Bulusu V S L N Bhaskara Teja, Bonam Ajay Saatvik, Pavan Puppala

Abstract-Stroke remains the second leading cause of death worldwide, underscoring the necessity for timely and precise predictive models to support early intervention. This research investigates advanced machine learning techniques to enhance stroke prediction accuracy. Initially, traditional classifiers such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) were employed. To further strengthen predictive performance, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LGBM) were later introduced. Model performance was assessed using various evaluation metrics, including accuracy, sensitivity, error rates, and log loss. Results indicate that XGBoost achieved an outstanding accuracy of 98%, while LGBM also played a crucial role in boosting overall predictive accuracy. These findings highlight the significant impact of sophisticated machine learning models in stroke prediction. By integrating state-of-the-art predictive analytics into clinical settings, this study aims to facilitate faster and more accurate diagnoses, ultimately improving patient care and advancing stroke detection methodologies.

DOI: /10.61463/ijset.vol.13.issue2.222