Prediction model of stroke recurrence based on support vector machine

Abstract

Stroke is a disease characterized by high morbidity, high disability and high mortality. In recent years, with the progress of society and the improvement of people's living standards, the cure and survival rate of stroke is gradually increasing, but at the same time, cases of recurrent stroke have also increased. Among the patients who have experienced recurrent stroke, it often leads to higher mortality and disability than those first-ever stroke patients. Therefore, it is important to improve the risk awareness of patients and medical workers, and to make scientific and accurate predictions on whether patients relapse, which is of great significance for the prevention and treatment of stroke. The purpose of this paper is to analyze the recurrence of stroke patients by analyzing the medical data of stroke patients, and to establish and develop a stroke prediction model based on Support Vector Machine (SVM). This model provides a basis for recurrence warning of stroke patients and rational allocation of medical resources.