Stroke Risk Prediction and Assessment Based on Big Aata Analysis

Stroke is a common cardiovascular and cerebrovascular disease with high morbidity, high mortality and high disability rate. In this paper, a stroke risk prediction and evaluation model based on support vector machine, random forest, BP neural network and genetic algorithm optimization neural network algorithm was established by using a raw dataset including 10 characteristic variables such as gender, age, hypertension, heart disease, and 1 stroke target variable. The experimental results show that the average blood glucose level, body mass index, hypertension and other variables have a great impact on the risk of stroke, and the neural network algorithm optimized by the genetic algorithm performs slightly better than the other three models.