Combining ECG image processing with ensemble learning techniques can improve diagnostic reliability and support clinical decision-making for cardiovascular disease prediction.
Combining ECG image processing with ensemble machine learning techniques has the potential to improve diagnostic reliability for cardiovascular disease prediction.
This review paper presents a comprehensive analysis of cardiovascular disease prediction using ECG image processing and ensemble machine learning techniques. Cardiovascular diseases remain one of the leading causes of mortality worldwide, making early and accurate diagnosis essential for improving patient outcomes. The study reviews ECG image preprocessing methods, lead segmentation, contour-based signal extraction, feature engineering, Principal Component Analysis (PCA) for dimensionality reduction, and ensemble machine learning approaches including Support Vector Machines, Random Forests, k-Nearest Neighbors, Gaussian Naive Bayes, and Logistic Regression. The paper also discusses system architecture, performance evaluation, advantages, limitations, explainable AI considerations, and future research directions. The findings indicate that combining ECG image processing with ensemble learning techniques can improve diagnostic reliability and support clinical decision-making. This review highlights the potential of artificial intelligence-driven cardiovascular disease prediction systems in modern healthcare environments.
M et al. (2026) conducted a review in Cardiovascular disease. ECG image processing and ensemble machine learning techniques was evaluated on Cardiovascular disease prediction. Combining ECG image processing with ensemble learning techniques can improve diagnostic reliability and support clinical decision-making for cardiovascular disease prediction.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: