Cardiovascular Disease Detection in ECG Images: A Comprehensive Analysis of Machine Learning and Deep Learning Approaches

Abstract

Cardiovascular diseases (CVDs) represent a major global health burden, necessitating accurate and early diagnosis for effective treatment. Traditional methods of ECG analysis rely heavily on expert interpretation, which is often time-consuming and prone to variability. This study proposes a hybrid deep learning model that combines visual analysis of electrocardiogram (ECG) images with structured clinical data to enhance cardiovascular disease detection. Convolutional neural networks (CNNs) such as AlexNet and SqueezeNet are utilized to extract salient features from ECG images, while a fully connected neural network (FCNN) processes patient metadata, including age, blood pressure, cholesterol levels, and medical history. The fusion of image-based and tabular data within a unified model yields a more holistic diagnostic system. The proposed model demonstrates improved classification accuracy and robustness compared to traditional approaches. With its potential to support early diagnosis and personalized treatment planning, this work contributes significantly toward intelligent healthcare solutions. Cardiovascular diseases remain a leading cause of mortality worldwide, necessitating accurate and early diagnosis. This study proposes a hybrid deep learning framework that combines ECG image analysis with structured patient medical data to improve the classification of CVDs. Convolutional neural networks (CNNs) are used to extract features from ECG images, while a fully connected neural network (FCNN) processes clinical data such as age, blood pressure, and cholesterol. The fusion of visual and non-visual data enhances diagnostic accuracy, supporting early intervention and personalized treatment.

Introduction

Cardiovascular diseases (CVDs) are the leading global cause of death, with early and accurate detection being crucial for effective treatment. Electrocardiograms (ECGs) are vital diagnostic tools but require expert interpretation, which is often unavailable in resource-limited settings. To overcome this, the study proposes a deep learning-based hybrid diagnostic system that combines ECG image analysis with clinical data for improved accuracy and contextual decision-making.

Key Components:

• Deep Learning with CNNs:

  • CNNs are effective for analyzing ECG images due to their spatial pattern recognition abilities.

  • A dual-branch CNN architecture (stack and full branches) is proposed, using 38 layers and LeakyReLU activations to prevent neuron inactivity.

  • Final output classifies ECGs into four categories: Normal (NP), Arrhythmia (AH), Myocardial Infarction (MI), and History of MI (HMI).

• Machine Learning (ML) Techniques:

  • Traditional ML models like Decision Trees (DT), SVM, K-NN, and Naïve Bayes (NB) were tested with datasets such as UCI Cleveland and South African Heart Disease dataset.

  • Feature selection methods (ANOVA, Lasso, etc.) significantly improved model performance.

  • Fusion of demographic data with ECG signals enhanced prediction accuracy.

• Pretrained Models & Transfer Learning:

  • Lightweight pretrained networks (e.g., SqueezeNet, AlexNet) were used for feature extraction and transfer learning.

  • These models were effective even on CPUs and could support low-resource environments.

• K-Nearest Neighbors (KNN) Algorithm:

  • KNN was applied as a simple yet effective classification tool, based on measuring distances from labeled data points.

• Literature Insights:

  • Previous studies showed deep learning outperforms traditional ML in ECG interpretation, especially when combining clinical and signal data.

  • Image processing techniques (e.g., CLAHE, FFT) further enhanced model robustness.

Outcomes & Benefits:

• The proposed system offers automated, accurate, and scalable CVD diagnosis, particularly suited for environments lacking specialized healthcare professionals.

• Integration with IoT and mobile platforms is feasible, enhancing real-time diagnostics.

• Future work includes improving model generalizability, incorporating more diverse data (e.g., genetic, lifestyle), and expanding multi-modal analysis.

Conclusion

The titled \" Cardiovascular Disease Detection in ECG Images: A Comprehensive Analysis of Machine Learning and Deep Learning Approaches\" leverages state-of-the-art deep learning methodologies to automate the identification of cardiovascular abnormalities through analysis of ECG images. This system is designed with a dual-user architecture, supporting both super administrators and regular users, thus ensuring efficient data management and ease of interaction. The platform enables super admins to monitor user activity, manage access, and oversee the system\'s overall performance. Simultaneously, end users can effortlessly upload ECG images and obtain detailed, AI-generated diagnostic reports. The integration of patient history further enriches the analysis, offering valuable clinical context that enhances the accuracy of diagnostic decisions. The system employs advanced deep learning algorithms to automate the detection and classification process, significantly reducing the reliance on manual ECG interpretation. This not only accelerates diagnosis but also minimizes human error, thereby contributing to improved patient care and outcomes. As visualized in Figures 7.1 and 7.2, the model achieves high accuracy while maintaining efficient loss optimization, showcasing its robustness and reliability.

References

[1] M. Mohammad D Abdubaker, “Detection of Cardiovascular Disease from ECG Image Using Machine Learning and Deep Learning methods ”,IEEE Transactions on Artificial Intelligence – 2 April 2023 vol 2 [2] M. Swathy and K. Saruladha, ”A comparative study of classification and prediction of Cardio-Vascular Diseases (CVD) using Machine Learning and Deep Learning tech- niques”, ICT Express, vol. 8, no. 1, pp. 109-116, 2022 [3] M. B. Abubaker and B. Babayig?it, ”Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods”, IEEE Transactions on Artificial Intelligence, vol. 4, no. 2, pp. 373-382, April 2023 [4] K. Fatema, S. Montaha, M.A.H. Rony, S. Azam, M. Z Hasan and M. Jonkman, ”A ro- bust framework combining image processing and deep learning hybrid model to classify cardiovascular diseases using a limited number of paper-based complex ECG images”, Biomedicines, vol. 10, no. 11, pp. 2835, 2022. [5] K. Prashant, P. Choudhary, T. Agrawal and E. Kaushik, ”OWAE-Net: COVID-19 detec- tion from ECG images using deep learning and optimized weighted average ensemble technique”, Intelligent Systems with Applications, vol. 16, pp. [6] Mohammed B. Abubaker and Bilal Babayig?it “ Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods” [7] World Health Organization (WHO), “Cardiovascular diseases,” Jun. 11, 2021. Accessed: Dec. 27, 2021. [Online]. Available: https://www.who. int/health-topics/cardiovascular-diseases [8] Government of Westren Australia, Department of Health, “Com- mon medical tests to diagnose heart conditions,” Accessed: Dec. 29, 2021. [Online]. Available: https://www.healthywa.wa.gov.au/Articles/A_ E/Common-medical-tests-to-diagnose-heart-conditions [9] M. Swathy and K. Saruladha, “A comparative study of classification and prediction of cardio-vascular diseases (CVD) using machine learning and deep learning techniques,” ICT Exp., to be published, 2021. [Online]. Available: https://doi.org/10.1016/j.icte.2021.08.021

Copyright

Copyright © 2025 Mr. Nilesh Dagadu Navale, Dr. Tripti Arjariya. This 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.