Exploring the Application of Machine Learning Algorithms in Stroke Prediction

DOI

10.2991/978-94-6463-540-9_12How to use a DOI?

Keywords

Stroke prediction; Machine learning; Personalized medicine

Abstract

Strokes significantly affect global public health and economic stability, with over 12.2 million new cases annually and being a leading cause of death worldwide. This essay takes a dataset containing 15000 entries and 22 features as an example to analyze its preprocessing, feature engineering, and algorithm optimization processes. The relevant results indicate that ML has the ability to identify complex risk patterns and provide personalized health interventions, which greatly advances stroke prediction and prevention strategies. In addition, the study evaluated the model using strict indicators such as accuracy, sensitivity, and Receiver Operating Characteristic and Area Under the Curve score to ensure reliable and applicable results. The significance of this research lies in its contribution to personalized medicine, highlighting how ML can be pivotal in developing targeted treatments and preventive measures. By improving early detection and enabling tailored healthcare solutions, the study enhances individual patient care and optimizes resource allocation across public health systems, setting a benchmark for future research in medical Artificial Intelligence applications.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qiyuan Dong
PY  - 2024
DA  - 2024/10/16
TI  - Exploring the Application of Machine Learning Algorithms in Stroke Prediction
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 98
EP  - 109
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_12
DO  - 10.2991/978-94-6463-540-9_12
ID  - Dong2024
ER  -