Stroke has diverse clinical manifestations and complex causes,characterized by high incidence,high disability rate,high recurrence rate,high mortality rate,and high economic burden.Currently,conventional clinical diagnostic and treatment methods face challenges such as difficulty in predicting disease and prognosis,low diagnostic accuracy,and slow treatment due to limitations in manpower and time.With the in-depth research in artificial intelligence and its application in the medical field,machine learning models can not only predict and diagnose stroke more accurately but also identify risk factors and determine high-risk populations.This paper reviews the current research status of machine learning algorithms,the identification of stroke risk factors,common machine learning algorithms for stroke prediction,and the effectiveness of these algorithms in stroke risk prediction.Findings from this paper will help provide a scientific basis for the early identification of high-risk populations,the adoption of effective preventive measures,and the formulation of precise treatment plans.
NETL
NSTL
万方数据
