IRO Journals
Journal of Ubiquitous Computing and Communication Technologies

Heart Disease Prediction Using EfficientNet-B0: A Deep Learning-Based ECG Image Analysis Approach

Open Access
Published: 13 July, 2026
Pages: 198-213
  • Prabakaran S. , Prabakaran S.
    Research Scholar, University College of Engineering Villupuram, (A Constituent College of Anna University, Chennai), India
    Research Scholar, University College of Engineering Villupuram, (A Constituent College of Anna University, Chennai), India
  • Kavitha E. , Kavitha E.
    Assistant Professor (Sl.Gr), University College of Engineering Villupuram, (A Constituent College of Anna University, Chennai), India
    Assistant Professor (Sl.Gr), University College of Engineering Villupuram, (A Constituent College of Anna University, Chennai), India
  • Anusha R. , Anusha R.
    Research Scholar, University College of Engineering Villupuram, (A Constituent College of Anna University, Chennai), India
    Research Scholar, University College of Engineering Villupuram, (A Constituent College of Anna University, Chennai), India
  • Arun Prasad S. Arun Prasad S.
    Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), St. Anne's College of Engineering and Technology, Panruti, India
    Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), St. Anne's College of Engineering and Technology, Panruti, India
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How to Cite

S., Prabakaran, Kavitha E., Anusha R., and Arun Prasad S. 2026. “Heart Disease Prediction Using EfficientNet-B0: A Deep Learning-Based ECG Image Analysis Approach”. Journal of Ubiquitous Computing and Communication Technologies 8 (3): 198-213. https://doi.org/10.36548/jucct.2026.3.003.
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Keywords

EfficientNet-B0
Cardiovascular Disease
Deep Learning
ECG Image Classification
Electrocardiogram
Transfer Learning

Abstract

Autonomous Electrocardiogram (ECG) image classification contributes to the effective detection of cardiac abnormalities and decrease the reliance on manual interpretations. This research study integrates transfer learning and EfficientNet-B0 architecture for the classification of four-class ECG images. In the pre-trained model, the open-source ECG images are pre-processed via image size adjustment, tensor conversion, and channel wise normalization. EfficientNet-B0 architecture combined with the weights obtained via ImageNet training was fine-tuned by enabling a task specific classifier with four output classes and training with cross-entropy loss function and Adam optimizer. The model performance analysis is done in terms of accuracy, precision, recall, F1-score, and confusion matrix analysis. The resultant ECG image classification corresponds to the accuracy of 99.61%. The class-level performance demonstrates high model prediction results. Moreover, the proposed framework performs efficient classification and visualization of individual ECG images.

References

  1. Ramesh, B., and Kuruva Lakshmanna. "A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System." IEEE Access 2024, vol. 12, 26683-26695.
  2. Oyewola, David Opeoluwa, Emmanuel Gbenga Dada, and Sanjay Misra. "Diagnosis of Cardiovascular Diseases by Ensemble Optimization Deep Learning Techniques." International Journal of Healthcare Information Systems and Informatics (IJHISI) 2024, vol. 19, no. 1, 1-21.
  3. Mayourian, Joshua, Amr El-Bokl, Platon Lukyanenko, William G. La Cava, Tal Geva, Anne Marie Valente, John K. Triedman, and Sunil J. Ghelani. "Electrocardiogram-Based Deep Learning to Predict Mortality in Paediatric and Adult Congenital Heart Disease." European Heart Journal 2025, vol. 46, no. 9, 856-868.
  4. Kaur, Dhamanpreet, J. Weston Hughes, Albert J. Rogers, Guson Kang, Sanjiv M. Narayan, Euan A. Ashley, and Marco V. Perez. "Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure." Circulation: Heart Failure 2024, vol. 17, no. 1, e010879.
  5. Almazroi, Abdulwahab Ali, Eman A. Aldhahri, Saba Bashir, and Sufyan Ashfaq. "A Clinical Decision Support System for Heart Disease Prediction Using Deep Learning." IEEE Access 2023, vol. 11, 61646-61659.
  6. Almulihi, Ahmed, Hager Saleh, Ali Mohamed Hussien, Sherif Mostafa, Shaker El-Sappagh, Khaled Alnowaiser, Abdelmgeid A. Ali, and Moatamad Refaat Hassan. "Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction." Diagnostics 2022, vol. 12, no. 12, 3215.
  7. Dubey, Animesh Kumar, Amit Kumar Sinhal, and Richa Sharma. "Heart Disease Classification Through Crow Intelligence Optimization-Based Deep Learning Approach." International Journal of Information Technology 2024, vol. 16, no. 3, 1815-1830.
  8. Omankwu, Obinnaya CB, M. C. Okoronkwo, and Chigbundu Kanu. "Cardiovascular (Heart) Diseases Prediction Using Deep Learning Neural Network Model." NIPES-Journal of Science and Technology Research 2024, vol. 6, no. 1.
  9. Zhang, Huanhuan, and Yufei Qie. "Applying Deep Learning to Medical Imaging: A Review." Applied Sciences 2023, vol. 13, no. 18, 10521.
  10. Amiri, Zahra, Arash Heidari, Nima Jafari Navimipour, Mansour Esmaeilpour, and Yalda Yazdani. "The Deep Learning Applications in IoT-Based Bio-And Medical Informatics: A Systematic Literature Review." Neural Computing and Applications 2024, vol. 36, no. 11, 5757-5797.
  11. Martin, Seth S., Aaron W. Aday, Norrina B. Allen, Zaid I. Almarzooq, Cheryl AM Anderson, Pankaj Arora, Christy L. Avery "2025 Heart Disease and Stroke Statistics: A Report of US And Global Data from the American Heart Association." Circulation 2025, vol. 151, no. 8, e41-e660.
  12. Rahman, Anichur, Tanoy Debnath, Dipanjali Kundu, Md Saikat Islam Khan, Airin Afroj Aishi, Sadia Sazzad, Mohammad Sayduzzaman, and Shahab S. Band. "Machine Learning and Deep Learning-Based Approach in Smart Healthcare: Recent Advances, Applications, Challenges and Opportunities." AIMS public health 2024, vol. 11, no. 1, 58.
  13. ECG Image [Dataset] - https://www.kaggle.com/datasets/kanishkarathore1604/ecg-image