Prediction Model of Ischemic Stroke Based on Machine Learning

Abstract

Machine learning (ML) can be used for deep mining and analysis of multidimensional medical data. At present, it has been widely used in medical diagnosis and prognosis prediction. This paper aims to make the existing research no longer focus on identifying key risk factors of stroke, and predict stroke risk more accurately. We collected the data of 3,962 cerebral apoplexy patients from 2019 to 2020, according to gender (male: 2,613; female: 1,349) and age (16–40 years old; 41–54 years old; 55–69 years old; 70 years old and above) layered. After data preprocessing, a stroke risk prediction model was built using principal component analysis (PCA) and extreme learning institutions (ELM). The prediction accuracy of PCA-ELM was as high as 97%. In this model, total cholesterol and high density lipoprotein are taken as 10 important factors that affect the incidence of stroke. This method can timely and efficiently mine the factors influencing the incidence of cerebral apoplexy from the data, and can predict the incidence of cerebral apoplexy. It has high value in practical application. This paper has great reference value in the research of brain death.