Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.
Sensors (Basel). 2022 Jun 21;22(13):4670. doi: 10.3390/s22134670.
A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.
当血流突然中断到大脑的某个部位时，就会发生中风。没有血液供应，脑细胞会逐渐死亡，并且根据受影响的大脑区域的不同而出现残疾。早期识别症状可以为中风的预测和促进健康生活提供有价值的信息。在这项研究工作中，借助机器学习（ML），开发和评估了多个模型，以设计用于中风发生的长期风险预测的稳健框架。这项研究的主要贡献是一种堆叠方法，该方法通过各种指标（例如 AUC、精度、召回率、F 度量和准确性）验证了其高性能。实验结果表明，堆叠分类的表现优于其他方法，其 AUC 为 98.9%，F 度量、精度和召回率为 97.4%，准确率为 98%。
