Cardiovascular Disease Prediction Model Based on Logistic Regression and Euclidean Distance

Cardiovascular disease is the number one killer threatening human life and health. Globally, about 40% of deaths are caused by cardiovascular disease. This paper builds a model based on logistic regression method and similarity evaluation based on distance formula to predict cardiovascular disease. The data in this article comes from the kaggle platform. In the experiment, we used the logistic regression algorithm to build the first-stage model. The accuracy and recall rate of the model reached 88.5% and 89.2%. Then using the similarity evaluation method based on Euclidean distance, we optimize the data that cannot be processed in the first stage. Finally, a complete classification model is obtained. Finally, we bring the test data into the complete model. The recall rate of the model finally reached 98%, and the prediction accuracy rate also reached 98%.