Fusing Attention and Convolution: A Hybrid Model for Brain Stroke Prediction - Bhuvanya, R.; Kujani, T.; Sivakumar, K. (EAI Endorsed Transactions on Scalable Information Systems, 2024)
INTRODUCTION: A stroke, a sudden interruption of blood flow to the brain, is a leading cause of disability and death. Early diagnosis is paramount for minimizing brain damage and maximizing treatment effectiveness.OBJECTIVES: Traditional diagnostic methods can be time-consuming and have limited Accuracy. METHODS: This study investigates the efficacy of various machine-learning models for stroke prediction. Specifically, it compares established models like K-Nearest Neighbor, Artificial Neural Network, Long Short Term Memory (LSTM), and stacked LSTM with a newly proposed Transformer Convolutional Neural Network (TCNN) architecture, which fuses Transformer and Convolutional neural network (CNN) models.RESULTS: The TCNN demonstrates significant promise, achieving a superior accuracy of 98% when optimized with the AMSGrad optimizer.CONCLUSION: These findings suggest that the TCNN architecture has the potential to revolutionize stroke prediction accuracy compared to existing methods, potentially leading to improved patient outcomes.
Keywords: AdaGrad; Brain Stroke detection; CNN; Machine Learning; Transformer