Risk Stratification for Stroke Using Attention Transformer Model

Stroke is a significant health concern with high morbidity and mortality rates. Early risk stratification is crucial for improving patient outcomes and guiding appropriate interventions. In this work, we demonstrate the transformer model for the dataset, aiming to enhance the accuracy of stroke risk prediction. This study focuses on leveraging the power of the transformer model to capture intricate patterns and dependencies within patient data. We employ a large dataset comprising various risk factors include heart disease, hypertension, age, gender, and other relevant clinical parameters. We leverage the Transformer-based architecture to deal with sequential patient data, offering a comprehensive perspective of medical histories and improving the model's capacity to detect minor but critical trends. The transformer's attention mechanism facilitates the model's focus on relevant aspects, ensuring comprehensive data utilization. The proposed model demonstrates the potential for enhancing stroke risk stratification and has the potential to drive advancements in personalized medicine and stroke management practices.