Evaluate Machine Learning Techniques for Early Disease of Cardiovascular Disease
subject: Early; Heart Disease Prediction; Machine Learning
description: Cardiovascular diseases are one of the major causes of death around the world, and their early detection is critical for effective intervention. The paper presents a systematic review of machine learning techniques used for the early prediction of cardiovascular diseases, focusing on studies carried out between 2019 and 2024. Widely used models considered in the review include Logistic Regression, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gradient Boosting, and hybrid ensemble methods with the aim of ascertaining predictive accuracy, interpretability, and clinical relevance. In most of the reviewed studies, ensemble and Random Forest models attained the highest accuracies of 90% - 98%, while Gradient Boosting and SVMs were mostly above 90% in balanced datasets. Logistic Regression had a moderate accuracy of 85%-91% but remained the most interpretable, while KNN established the lowest performance of 80%-86%. Despite the promising strides, there are a number of limitations, such as imbalance in datasets, limited external validation, and small benchmark datasets, that are limiting general application in health. This systematic review highlights strengths and weaknesses of the contemporary machine learning approaches and makes it evident that clinically validated, interpretable, and generalizable models should be developed in order to assist real-world medical decision-making. 2025 IEEE.
creator: Sushmitha, M.T.; Saxena, Surabhi; Singhal, Neha
source: 5th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155664-8;
language: English
type: Conference paper
coverage: Sushmitha M.T., Christ University, Department of Computer Science, Bangalore, India; Saxena S., Christ University, Department of Computer Science, Bangalore, India; Singhal N., Christ University, Department of Computer Science, Bangalore, India