International Journal of Research and Development in Engineering Sciences (IJRDES ) | Enhancing Brain Stroke Prediction Using Machine Learning for Early Intervention

Enhancing Brain Stroke Prediction Using Machine Learning for Early Intervention

Stroke is still a significant global health concern that requires sophisticated predictive methods for early detection and treatment. In this work, a novel machine learning (ML) framework for automated stroke prediction is presented, and its accuracy and generalization skills are evaluated against those of six well-known classifiers. In order to guarantee clear decision-making in clinical applications, SHAP and LIME approaches are also used to highlight model interpretability. By combining local and global analytical approaches, the suggested framework improves the standardization of intricate machine learning models. Notably, Random Forest routinely achieves higher predicting accuracy than other algorithms. An enhanced ensemble strategy that uses a voting mechanism to leverage numerous classifiers and incorporates CATBOOST and a Stacking Classifier is presented in order to further increase performance. This study offers a thorough and trustworthy approach to early stroke diagnosis and treatment, which will ultimately lessen the serious health and financial effects of this common illness.

Research Type: Inductive Research
Paper Type: Interpretative Paper
Vol.7 , Issue 1 , Pages: 46 - 52, Feb 2025
Published on: 23 Feb, 2025
Issue Type: Regular

Cite Score

:

100
No. of authors

:

75
No. of Downloads

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43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

About Authors:

BOKKA SURI BABU

India

Viswam Engineering College

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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Corresponding Author: BOKKA SURI BABU, suribabubokka801@gmail.com

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Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.