Document Type
Article
Publication Date
2025
DOI
10.1371/journal.pone.0328967
Publication Title
PLoS One
Volume
20
Issue
8
Pages
e0328967 (1-31)
Abstract
Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing models were found. Afterward, ensemble machine learning methods were used to find the most accurate predictions using the top 5 models ranked by shapely value. The research demonstrates an impressive accuracy of 92.39%, surpassing other proposed models' performance. This study highlights the utility of combining game theory and machine learning in Ischemic stroke prediction and the potential of ensemble learning methods to increase predictive accuracy in ischemic stroke analysis.
Rights
© 2025 Chakraborty et al.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability
Article states: "The anonymised data collected are available as open data via the Brain stroke prediction dataset on Kaggle online data repository: https://www.kaggle.com/datasets/zzettrkalpakbal/full-filled-brain-stroke-dataset/."
Original Publication Citation
Chakraborty, P., Bandyopadhyay, A., Parui, S., Swain, S., Banerjee, P. S., Si, T., Qin, H., & Mallik, S. (2025). OptiSelect and EnShap: Integrating machine learning and game theory for ischemic stroke prediction. PLoS One, 20(8), 1-31, Article e0328967. https://doi.org/10.1371/journal.pone.0328967
Repository Citation
Chakraborty, P., Bandyopadhyay, A., Parui, S., Swain, S., Banerjee, P. S., Si, T., Qin, H., & Mallik, S. (2025). OptiSelect and EnShap: Integrating machine learning and game theory for ischemic stroke prediction. PLoS One, 20(8), 1-31, Article e0328967. https://doi.org/10.1371/journal.pone.0328967
ORCID
0000-0002-1060-6722 (Qin)
Included in
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Artificial Intelligence and Robotics Commons, Emergency Medicine Commons, Graphics and Human Computer Interfaces Commons, Theory and Algorithms Commons