A Stacking-Based Heart Disease Classification Prediction Model

Heart disease is one of the leading causes of death globally, and early diagnosis and prevention of heart disease are of great significance. The paper provides a correlation analysis of the quantitative and qualitative variables on a heart disease dataset. Based on the Pearson correlation coefficient matrix, the paper determines the base classifiers and uses logistic regression as the meta-classifier to construct a stacking ensemble learning model for heart disease classification prediction. To address the issue of data imbalance, cost-sensitive learning is further introduced, and a stacking heart disease classification prediction model based on threshold optimization is established. The experimental result shows that the stacking heart disease classification prediction model achieves a classification accuracy of 90.16% and demonstrates good generalization ability. Our work provides guidance for further research on heart disease classification prediction.