Efficient Machine Learning Algorithms for Cardiovascular Risk Prediction
Received: 17 June 2025 | Revised: 1 July 2025, 8 July 2025, and 11 July 2025 | Accepted: 13 July 2025 | Online: 11 September 2025
Corresponding author: Sreerama Murty Maturi
Abstract
Cardiovascular Disease (CVD) is a critical global health concern requiring efficient early prediction. This study evaluates Machine Learning (ML) algorithms—Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (K-NN)—for forecasting the cardiovascular risk. By analyzing key patient features, such as cholesterol, blood pressure, and lifestyle, these models classify individuals into low or high-risk categories. The findings demonstrate that LGBM and RF achieve superior performance, both reaching 99% accuracy, precision, recall, and F1-score, alongside high ROC-Area Under the Curve (AUC) values. This research provides robust, data-driven tools to enhance the timely diagnosis and support preventative medical strategies, ultimately improving the patient outcomes.
Keywords:
heart disease prediction, machine learning, early diagnosis, real-time prediction systems, healthcare AIDownloads
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Copyright (c) 2025 V. Sitharamulu, Sreerama Murty Maturi, M. Murugesan, Mahammad Rafi Dudekula, Hanumantha Rao Battu
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