Heart disease exploration and prediction using data mining technique

Abstract

In recent years, the incidence rate of various types of heart disease is increasing, so more and more studies focus on the influencing factors and prediction of heart disease. This paper investigates the relationship between heart disease and 21 representative variables to explore risk factors and predict the heart attack. First, through comparative analysis, we find that there is a significant difference in the distribution of variables. The findings highlight factors associated with heart disease, such as underlying diseases, lifestyle habits, and medical conditions. Secondly, these four factors (health condition, environment, age, and BMI) are extracted through factor analysis. Thirdly, a binary logistic regression prediction model is established to study the relationship between variables. Finally, the combination of multiple analysis methods in this study is a novel approach that can be used for exploring heart disease factors and predicting disease onset.