Mitu Mostarina, Hasan S M Mahedy, Uddin Md Palash, Mamun Md Al, Rajinikanth Venkatesan, Kadry Seifedine
Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh.
Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
Comput Methods Biomech Biomed Engin. 2025 Jun;28(8):1223-1242. doi: 10.1080/10255842.2024.2316877. Epub 2024 Feb 21.
The death of brain cells occurs when blood flow to a particular area of the brain is abruptly cut off, resulting in a stroke. Early recognition of stroke symptoms is essential to prevent strokes and promote a healthy lifestyle. FAST tests (looking for abnormalities in the face, arms, and speech) have limitations in reliability and accuracy for diagnosing strokes. This research employs machine learning (ML) techniques to develop and assess multiple ML models to establish a robust stroke risk prediction framework. This research uses a stacking-based ensemble method to select the best three machine learning (ML) models and combine their collective intelligence. An empirical evaluation of a publicly available stroke prediction dataset demonstrates the superior performance of the proposed stacking-based ensemble model, with only one misclassification. The experimental results reveal that the proposed stacking model surpasses other state-of-the-art research, achieving accuracy, precision, F1-score of 99.99%, recall of 100%, receiver operating characteristics (ROC), Mathews correlation coefficient (MCC), and Kappa scores 1.0. Furthermore, Shapley's Additive Explanations (SHAP) are employed to analyze the predictions of the black-box machine learning (ML) models. The findings highlight that age, BMI, and glucose level are the most significant risk factors for stroke prediction. These findings contribute to the development of more efficient techniques for stroke prediction, potentially saving many lives.
当大脑特定区域的血流突然中断时，脑细胞就会死亡，从而导致中风。早期识别中风症状对于预防中风和促进健康的生活方式至关重要。FAST测试（观察面部、手臂和言语方面的异常）在诊断中风的可靠性和准确性方面存在局限性。本研究采用机器学习（ML）技术来开发和评估多个ML模型，以建立一个强大的中风风险预测框架。本研究使用基于堆叠的集成方法来选择最佳的三个机器学习（ML）模型，并结合它们的集体智慧。对一个公开可用的中风预测数据集进行的实证评估表明，所提出的基于堆叠的集成模型具有卓越的性能，只有一次错误分类。实验结果表明，所提出的堆叠模型超越了其他现有研究，准确率、精确率、F1分数达到99.99%，召回率达到100%，接收器操作特征（ROC）、马修斯相关系数（MCC）和卡帕分数为1.0。此外，还采用了夏普利值附加解释（SHAP）来分析黑箱机器学习（ML）模型的预测结果。研究结果突出表明，年龄、体重指数和血糖水平是中风预测中最重要的风险因素。这些发现有助于开发更有效的中风预测技术，有可能挽救许多生命。
