Early-Stage Cardiovascular Disease Prediction Using a Sigmoidtropy-Based Decision Tree

Abstract

Heart disease (HD) is a significant health issue in the world, and its early and proper prediction is essential to minimize mortality and the development of the disease. Cardiovascular disease (CVD) is one of the diseases that need effective and stable predictive models to assist clinical decision-making. This paper gives a Sigmoidtropy-Based Decision Tree (SDT) model of cardiovascular disease prediction, which improves the traditional decision tree by adding a sigmoid-based formulation of entropy. The heart disease data are first grouped by the K-means clustering method in order to enhance the data representation. The suggested SDT model is tested on the Cleveland heart disease dataset of the UCI repository and compared to the traditional classifiers, such as Naive bayes, random forest, and the traditional Decision Tree models. Experimental findings indicate that the SDT has an accuracy of 99.67 which is better than the performance of Random Forest (76.89%), Decision Tree (76.56%), and Naive Bayes (81.84%) with a lower execution time. Despite the promising performance shown by the results, it needs further validation with more datasets and strong evaluation plans to determine the generalizability