Predicting the risk of stroke based on machine learning

Abstract

Stroke, the second largest leading cause of death among all chronic diseases, is affecting about 101 million people in the world currently. It is estimated that this number of stroke cases will increase by 2.25 by the year 2050. Considering the large number of potential patients with stroke, a mathematical model is designed to predict ones risk of having a stroke in the future based on ones basic health data using machinery methods. Using the algorithm of Logistic Regression, the model reaches an accuracy of 92.28% when predicting whether one has a stroke, the model also validates that hypertension is the leading cause of the incidence of stroke by finding out the highest correlation value among all the feature variables. People who would like to know their probability of having a stroke can use the model, then they can have some precautionary measures to lower the likelihood of happening of stroke based on the prediction given, which helps save the medical costs and overuse of medical resources. Governments can enact policies and allocate medical resources based on the predictions made by the model.