Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{173915,
        author = {N.SRILEKHA and B.VANDANA and B.MANIKANTA LOKESHWAR and CH.TILAK ADITYA and K.VENKAT},
        title = {WEB-BASED SYSTEM FOR ECG ARRHYTHMIA DETECTION AND HEART DISEASE PREDICTION USING DEEP CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2171-2176},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173915},
        abstract = {The increasing prevalence of cardiovascular diseases necessitates the development of efficient diagnostic tools to enhance early detection and management. This paper presents a web-based system designed for ECG arrhythmia detection and heart disease prediction utilizing Deep Convolutional Neural Networks (D-CNNs).We classify ECG in to 5 categories, one being normal and the others include, Atrial Fibrillation, Atrial Flutter,Ventricular Tachycardia, and Ventricular Fibrillation .The proposed system leverages a user- friendly interface to facilitate real-time ECG data upload and analysis, enabling both healthcare professionals and patients to monitor cardiac health effectively.
The CNN model is trained on a diverse dataset comprising labelled ECG signals, allowing it to learn complex patterns associated with various arrhythmias and heart conditions. The system achieves high accuracy and sensitivity by using various algorithms, significantly outperforming traditional methods in both classification and prediction tasks. Additionally, the web-based platform ensures accessibility, enabling users to receive instant feedback and insights about their cardiac health from any location.},
        keywords = {Electrocardiogram, Convolutional Neural Network, Atrial Fibrillation, Atrial Flutter, Ventricular Tachycardia, and Ventricular Fibrillation},
        month = {March},
        }