The Comprehensive Investigation of Machine Learning-Based Patient Brain Stroke Prediction

Xiuyuan Wei

2024

Abstract

This paper aims to comprehensively review machine learning methodologies for stroke prediction, evaluating both traditional and deep learning approaches, and discussing challenges and potential solutions in this domain. The paper conducts a thorough examination of machine learning methodologies for stroke prediction. Traditional techniques are scrutinized for their efficacy in handling stroke prediction tasks across various datasets. Deep learning approaches such as U-Net and Generative Adversarial Networks are also investigated to assess their suitability and performance. Moreover, the review delves into the intricacies of these methods, considering factors such as interpretability, privacy concerns, and data quality issues. Additionally, it explores novel techniques such as the Shapley Addition Method of Interpretation and Federated Learning (FL) as potential solutions to enhance interpretability and protect patient privacy. The review also examines the potential of transfer learning to optimize model generalization across different domains, aiming to provide insights into the most effective methodologies for stroke prediction. Findings suggest the promise of machine learning in stroke prediction. Future research directions include integrating emerging techniques such as large language models and multimodal data fusion for improved accuracy guiding researchers and practitioners in selecting appropriate Machine Learning methods and addressing challenges in stroke prediction for enhanced patient care.

Paper Citation

in Harvard Style

Wei X. (2024). The Comprehensive Investigation of Machine Learning-Based Patient Brain Stroke Prediction. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 362-367. DOI: 10.5220/0012938200004508

in Bibtex Style

@conference{emiti24,
author={Xiuyuan Wei},
title={The Comprehensive Investigation of Machine Learning-Based Patient Brain Stroke Prediction},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={362-367},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012938200004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - The Comprehensive Investigation of Machine Learning-Based Patient Brain Stroke Prediction
SN - 978-989-758-713-9
AU - Wei X.
PY - 2024
SP - 362
EP - 367
DO - 10.5220/0012938200004508
PB - SciTePress