Chakraborty Pritam, Bandyopadhyay Anjan, Parui Sricheta, Swain Sujata, Banerjee Partha Sarathy, Si Tapas, Qin Hong, Mallik Saurav
School of Computer Engineering, Bhubaneswar, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India.
Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Mohanpur, Madhya Pradesh, India.
PLoS One. 2025 Aug 13;20(8):e0328967. doi: 10.1371/journal.pone.0328967. eCollection 2025.
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.
使用博弈论和机器学习技术进行中风分析。该研究调查了沙普利值在预测缺血性脑中风分析中的应用。首先，偏好算法确定各种机器学习模型中的最重要特征，包括逻辑回归、K近邻、决策树、支持向量机（线性核）、支持向量机（径向基核）、神经网络等。对于每个样本，评估并选择排名前3、4和5的特征以评估其性能。使用沙普利值方法根据模型的预测能力对使用其最佳四个特征的模型进行排名。结果，发现了性能更好的模型。之后，使用集成机器学习方法，利用按沙普利值排名的前5个模型来找到最准确的预测。该研究证明了令人印象深刻的92.39%的准确率，超过了其他提出的模型的性能。这项研究突出了将博弈论和机器学习相结合在缺血性中风预测中的效用，以及集成学习方法在提高缺血性中风分析预测准确性方面的潜力。
