Stroke prediction based on multifactorial regression models

Stroke is known as a disease that can cause patients’ limbs to be weak. The disease will be more serious when it is developed to Cerebral infarction. It can lead to facial numb, barylalia, blurred vision and Nausea. This article investigates the feasibility of judging whether a person will have stroke disease based on different examined factors. Specifically, the R project will be utilized to analyse a dataset by logistic regression, dummy variable analysis and Lasso regression. According to the analysis, the model of predicting stroke disease and its simplification will be presented. Based on the evaluation, main causalities of stroke will be clarified. Moreover, the percentage of right prediction of the prediction will be recorded to show the accuracy of the result. These results shed light on offering the possible causality criterions of predicting stroke, which paves simplify path to predict stroke.