Prediction of Brain Stroke Severity Using Machine Learning Techniques

DOI

10.2991/978-94-6463-858-5_17How to use a DOI?

Keywords

Stroke Prediction; Machine Learning; Stroke Severity

Abstract

Stroke is an illness that targets the arteries supplying blood to and in the brain. Stroke happens when a blood vessel that nourishes the brain is affected is clogged as a result of obstruction or hemorrhage. According to WHO Report, 3% population is affected by subarachnoid hemorrhage, 10% by intracerebral hemorrhage, and 87% by ischemic stroke. 80% of the cases are preventable so implementing proper education regarding the stroke signs is very crucial. The current research has limited potential for anticipating possible health risks related to different types of strokes. This research study recommends an early forecast of stroke diseases based on the use of various ML techniques with the prevalence of hypertension, BMI status, average glucose status, smoking, past history of stroke and age. ML algorithms such as Logistic Regression, Random Forest, Decision Trees, Naive Bayes, SVM, MLP etc. are applied to forecast the severity of future stroke incidence on a scale of 0 to 3. The research not only forecasts the future likelihood of a getting a stroke for a particular person who has never suffered from a stroke, but also the future likelihood of occurrence of a more hazardous type of stroke for those who have already suffered from a stroke.

Copyright

© 2025 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.

Download article (PDF)

TY  - CONF
AU  - Jallu Swathi
AU  - Jayalaxmi Anem
AU  - Sigalapalli Ruchitha
AU  - Sillasaikumar
AU  - Athmakuri Haritha
AU  - Ponnada Mohan Rao
PY  - 2025
DA  - 2025/11/04
TI  - Prediction of Brain Stroke Severity Using Machine Learning Techniques
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 187
EP  - 196
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_17
DO  - 10.2991/978-94-6463-858-5_17
ID  - Swathi2025
ER  -