Heart disease is a general term for heart diseases, including rheumatic heart disease, congenital heart disease, hypertensive heart disease, coronary heart disease, myocarditis and other heart diseases. As a major cause of death worldwide, it is important to further improve the prediction, monitoring, and effective prevention and treatment of heart disease based on the current level of science, technology, and medical care. This study is based on thirteen quantifiable factors influencing heart disease and conducts both overall logistic regression and paired logistic regression analyses. In the process of regression analysis, this paper highlights the statistical significance of each variable, and the results of the regression analysis are presented as a more accurate heart disease prediction model, which can be updated regularly to improve the timeliness of the heart disease prediction model, taking into account the influence of time factors. In addition to this, the paper also asserts the dilemmas and challenges faced by this research from other perspectives, such as genetics, lifestyle, etc., and realizes that in real life, researchers should consider the multidimensional influences of heart disease more comprehensively.
