Stroke Prediction Model Based on Decision Tree

Abstract

In this paper, the predictive model of stroke based on decision tree is implemented to predict the stroke probability of ten samples by using Python language. The dataset of stroke is collected and is preprocessed, then the Gini coefficients of each feature are calculated to select the division, and then the decision tree model is obtained. Finally, the stroke probability is predicted for ten samples. In addition, Naive Bayes model is applied to predict the stroke probability to compare with the decision tree method. The experimental results show that older people with high blood pressure, heart disease, habitual smoking are more possible to have stroke, with a prediction accuracy of 88% for decision tree method and 79% for Naive Bayes model, respectively.