Explore the application of machine learning algorithms in predicting stroke

Abstract

Stroke is an acute cerebrovascular disease that seriously endangers human health and life safety. It occurs suddenly and is not easy to cure. It is easy to leave sequelae after the disease, and the age of onset tends to be younger, becoming a major disease that threatens human life and quality of life. With the advent of the era of big data, people can process and analyze the data obtained from a large number of patients with cardiovascular and cerebrovascular diseases. Through Machine Learning (ML), they can master the risk factor indicators leading to stroke, so that they can effectively predict the incidence of stroke, help cardiovascular and cerebrovascular patients carry out preventive treatment as soon as possible, reduce the incidence or the symptoms. This paper introduces several classic ML algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), Gaussian Naive Bayesian, k-NN, Logical Regression, etc. In order to explore the accuracy of these algorithms, this study obtained 15,000 patient data from the Kaggle website, including 22 characteristics, and used the above algorithms to predict. Through the evaluation of Accuracy Score indicators, we have a clear understanding of the application of various algorithms in predicting stroke.