Cardiovascular Disease Prediction Using Machine Learning Ap-proaches

Abstract

Cardiovascular disease is a prominent contributor to global mortality. The timely identification and prognostication of cardiovascular disease can mitigate its incidence and diminish fatality ratios. The use of machine learning has emerged as a promising methodology for forecasting the likelihood of heart disease. The present study delves into the application of machine learning algorithms in the prediction of heart disease. In this study, a publicly accessible dataset on heart disease is utilized to assess the efficacy of various machine learning algorithms and determine the optimal models. The study involves a comparative analysis of various algorithms, namely Logistic Regression, Random Forest, Support Vector Machines, and Artificial Neural Networks, with respect to their accuracy and other performance metrics. The findings indicate that the Artificial Neural Network model yielded the highest level of performance, exhibiting an accuracy rate of 87.5%. The aforementioned showcases the prospective employment of machine learning in the domain of heart disease prognosis, thereby highlighting the exigency for additional inquiry in this field.