Application and comparison of decision tree algorithm and K-Nearest Neighbors algorithm in heart disease prediction

Abstract

In the past two decades, rapid industrialization and urbanization have led to tremendous economic growth and an improvement in people's living standards. However, the impact of people's irregular lifestyles and habits on their health has gradually emerged. Among them, cardiovascular diseases have become particularly prominent, with increasing incidence and mortality rates, especially in developing countries. Heart disease is a major cause of the rising death rates. Early-stage prediction of heart disease poses a major challenge in clinical analysis. Today, the adoption of appropriate decision support systems to achieve cost reduction in clinical trials has become a future development trend for many hospitals. This study compares decision tree classification and K-nearest neighbors (KNN) classification algorithms to seek better diagnostic performance for heart disease. The existing dataset of heart disease patients from the Cleveland database is used to te3st and demonstrate the performance of all algorithms, providing support for the establishment of a heart disease prediction system. This, in turn, can assist doctors in making more accurate diagnoses and timely interventions before the onset of heart disease, thereby reducing the mortality rate of heart disease from the source.