Application of Artificial Intelligence in Stroke Prediction: Latest Advancements and Future Prospects

Boxiang He

2024

Abstract

Some scientific and technological methods are more and more applied in medical treatment, and great achievements have been made in the prediction of stroke, but there are still great challenges. This paper explores the intersection of stroke management and Artificial Intelligence (AI), focusing on recent advancements, methodologies, limitations, and future prospects. Stroke, characterized by disrupted blood flow to the brain, necessitates swift diagnosis and intervention to mitigate potential cell damage or death. Traditional machine learning algorithms such as Support Vector Machine (SVM) and Random Forest, along with deep learning algorithms like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), have been employed to construct predictive models for stroke diagnosis and prognosis. However, challenges including interpretability, privacy concerns, and applicability across diverse healthcare settings persist. Solutions such as Shapley Additive Explanations (SHAP), federated learning, and transfer learning have been proposed to address these challenges and enhance the trustworthiness and generalizability of AI-driven approaches in stroke management. Continued research efforts are necessary to overcome limitations, expand sample sizes, and enhance the accuracy and efficiency of AI models in predicting and analyzing strokes, ultimately improving patient outcomes in stroke management.

Paper Citation

in Harvard Style

He B. (2024). Application of Artificial Intelligence in Stroke Prediction: Latest Advancements and Future Prospects. 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 617-621. DOI: 10.5220/0012960700004508

in Bibtex Style

@conference{emiti24,
author={Boxiang He},
title={Application of Artificial Intelligence in Stroke Prediction: Latest Advancements and Future Prospects},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={617-621},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012960700004508},
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 - Application of Artificial Intelligence in Stroke Prediction: Latest Advancements and Future Prospects
SN - 978-989-758-713-9
AU - He B.
PY - 2024
SP - 617
EP - 621
DO - 10.5220/0012960700004508
PB - SciTePress