Analysis of Machine Learning Techniques for Prediction of Cardiac Diseases
Year: 2025
Type: conference-paper
Abstract: Early detection and diagnosis are crucial because cardiovascular diseases are the leading cause of fatalities in the world. Timely diagnosis and intervention can significantly improve patient outcomes and reduce healthcare costs. In this study, we used Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbour, and Support Vector Machines to predict the risk of cardiovascular diseases based on two datasets obtained from the UCI library. We assessed each algorithm’s performance based on its accuracy, precision, recall, and F1 Score. Our findings show that KNN performed better than the other algorithms, with Accuracy of 90.16%. This study shows how machine learning algorithms can be used to predict cardiac diseases and offers guidance for further study in this area.
Source: Procedia Computer Science
Authors: Ayush Gupta, Ananya Singh, Pragya Kumar, Gautam Raj, Vivek Tomar
Institution: Graphic Era University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410301205