Research Status of Machine Learning in Stroke Treatment
DOI:
https://doi.org/10.62051/z3kdsp79Keywords:
Machine Learning; Random Forest; Support Vector Machine; Deep Learning.Abstract
Stroke, characterized by high morbidity, mortality and disability, seriously jeopardizes human health. Timely and accurate prediction and diagnosis of the disease is of great significance. Machine learning can efficiently analyze large-scale datasets and shows great potential for application at various stages in the treatment of stroke disease. In this paper, we start from the common data processing, feature selection, model training and evaluation of the conventional application process of machine learning. This paper analyzes the application of Random Forest, Support Vector Machine, and Deep Learning, which are commonly used machine learning models, in the diagnosis, treatment, and prognosis of stroke. This paper summarizes their advantages over traditional statistical methods and the current shortcomings and points out the appropriate methods for improvement. In addition, this paper also looks forward to the future application of machine learning in the field of stroke prediction. It explores the possible path of machine learning technology in the future development of the fight against stroke. This study aims to promote the progress of machine learning in the field of stroke research by integrating and comparing related research approaches, and so on.
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