Brain Stroke Prediction System

DOI

10.2991/978-94-6239-723-1_46How to use a DOI?

Keywords

Brain Stroke Prediction; Machine Learning; Artificial Intelligence; Explainable AI (XAI); SHAP; LIME; Logistic Regression; Random Forest; XGBoost

Abstract

Brain stroke is one of the major causes of death and disability, and this is mainly because of delayed diagnosis and restricted early opportunity evaluation. In conventional diagnosis, tools such as CT scans and MRI are used to detect stroke occurrence only after it has happened. Prediction models are also available, but only on limited information, which can be answered with a yes or no response. An explainable machine learning-based stroke prediction method is presented in this research. Various factors are considered by this system. To increase the accuracy of the proposed solution, fact-balancing tools like SMOTE and ADASYN are used with the Random Forest, Logistic Regression, and XGBoost algorithms. Medical trust is enhanced by explainable AI techniques like SHAP and LIME.

Copyright

© 2026 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  - Niteen Dhutraj
AU  - Bhushan Chaudhari
AU  - Divyani Patil
AU  - Shreya Patil
AU  - Chetana Patil
PY  - 2026
DA  - 2026/07/14
TI  - Brain Stroke Prediction System
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 513
EP  - 523
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_46
DO  - 10.2991/978-94-6239-723-1_46
ID  - Dhutraj2026
ER  -