Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Therefore, the aim of this work is to classify state-of-arts on ML techniques for brain stroke into 4 categories based on their functionalities or similarity, and then review studies of each category systematically. A total of 39 studies were identified from the results of ScienceDirect web scientific database on ML for brain stroke from the year 2007 to 2019. Support Vector Machine (SVM) is obtained as optimal models in 10 studies for stroke problems. Besides, maximum studies are found in stroke diagnosis although number for stroke treatment is least thus, it identifies a research gap for further investigation. Similarly, CT images are a frequently used dataset in stroke. Finally SVM and Random Forests are efficient techniques used under each category. The present study showcases the contribution of various ML approaches applied to brain stroke.

机器学习（ML）可以提供准确，快速的预测结果，并且已经成为健康设置中的强大工具，可以为中风患者提供个性化的临床护理。机器学习和深度学习在医疗保健中的应用正在增长，但是，尽管确实有研究的需要，但一些研究领域并未引起足够的重视以进行科学研究。因此，这项工作的目的是基于脑卒中的机器学习的功能或相似性，将机器学习的最新技术分为4类，然后系统地回顾每类的研究。从ScienceDirect网络科学数据库中有关2007年至2019年脑卒中的ML的结果中，总共鉴定出39项研究。获得支持向量机（SVM）作为10项卒中问题研究的最佳模型。除了，尽管中风治疗的数量最少，但在中风诊断中发现的研究最多，这为进一步研究确定了研究空白。同样，CT图像是笔画中经常使用的数据集。最后，SVM和随机森林是每个类别下使用的有效技术。本研究展示了应用于脑卒中的各种ML方法的贡献。