Year: 2024
Type: article
Abstract: 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.
DOI: 10.4108/eetsis.7022
Authors: R. Bhuvanya, T. Kujani, K. Sivakumar
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405294577