The Prophet Predicting Ischaemic Stroke: Transformer-Based Multimodal Classification

Yuhang Dong

2025

Abstract

Ischemic stroke is a common and extremely harmful cerebrovascular disease. It develops suddenly, has a high mortality rate and high disability rate, and seriously threatens the life and health of patients. Early and accurate prediction of stroke is of great significance for timely intervention and improving the success rate of treatment. With the continuous accumulation of medical data and the rapid development of artificial intelligence technology, stroke prediction models based on machine learning and deep learning have gradually become a research hotspot. Based on the MR CLEAN clinical trial dataset, this study constructed and trained three models with significant structural differences: a combination model of XGBoost and xDeepFM, a SSMMSRP model, and a TranSOP model, aiming to explore the performance differences of different modeling methods in stroke prediction tasks. Preliminary comparative experimental results show that the TranSOP model shows the best cost-effectiveness in various performance indicators and has good promotion potential and application prospects. This study provides multiple feasible paths for the construction of stroke prediction models, verifies the advantages of multimodal fusion models in this task, and lays the foundation for further optimization of clinical decision support systems.

Paper Citation

in Harvard Style

Dong Y. (2025). The Prophet Predicting Ischaemic Stroke: Transformer-Based Multimodal Classification. In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-792-4, SciTePress, pages 116-122. DOI: 10.5220/0014322100004718

in Bibtex Style

@conference{emiti25,
author={Yuhang Dong},
title={The Prophet Predicting Ischaemic Stroke: Transformer-Based Multimodal Classification},
booktitle={Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2025},
pages={116-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014322100004718},
isbn={978-989-758-792-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - The Prophet Predicting Ischaemic Stroke: Transformer-Based Multimodal Classification
SN - 978-989-758-792-4
AU - Dong Y.
PY - 2025
SP - 116
EP - 122
DO - 10.5220/0014322100004718
PB - SciTePress