IRO Journals
Journal of Trends in Computer Science and Smart Technology

Federated Attention‑Capsule CNN with Bio‑Optimization for Coronary Artery Disease Screening

Open Access
Published: 17 April, 2026
Pages: 195-218
  • Angelin Jeba P. , Angelin Jeba P.
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Jasmine David D. , Jasmine David D.
    Associate Professor, School of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
    Associate Professor, School of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
  • Selvarathi M. , Selvarathi M.
    Division of Mathematics, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Mathematics, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Kenneth Bryan Nesh , Kenneth Bryan Nesh
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Alan Biju Varkey , Alan Biju Varkey
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Jemima Jebaseeli T. Jemima Jebaseeli T.
    Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India
    Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India
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How to Cite

P., Angelin Jeba, Jasmine David D., Selvarathi M., Kenneth Bryan Nesh, Alan Biju Varkey, and Jemima Jebaseeli T. 2026. “Federated Attention‑Capsule CNN With Bio‑Optimization for Coronary Artery Disease Screening”. Journal of Trends in Computer Science and Smart Technology 8 (2): 195-218. https://doi.org/10.36548/jtcsst.2026.2.001.
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Keywords

Coronary Artery Disease
Federated Learning
Capsule Networks
Convolutional Neural Network
Genetic Algorithm
Particle Swarm Optimization

Abstract

Coronary artery disease (CAD) remains the leading cause of death around the globe and hence requires reliable, non-invasive, and secure diagnostic methods. In order to diagnose CAD in its early stages, we present a novel architecture of a Federated Attention-Capsule Convolutional Neural Network (FAC-CNN) integrated with Bio-Inspired Optimization Algorithms (BIOA) in dealing with multiple modalities of physiological data such as Electrocardiography (ECG), Photoplethysmogram (PPG), and Blood Pressure (BP). The model is evaluated using unimodal (only ECG signals) and multimodal physiological datasets to validate the proposed algorithm for different data distributions. To improve accuracy, the architecture of FAC-CNN uses capsule networks to recognize the hierarchical structure within the data fed into it. Through attention mechanisms, the network is capable of selecting the most important features related to cardiac diseases. To ensure data security, the federated learning (FL) method is applied as a solution to the problem of local model training using patients' private data stored in edge computing devices. A novel hybrid optimization method combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is used to update learning rates, the total number of capsule layers, and routing iterations. Three databases, including the PTB diagnostic ECG database, the MIT-BIH Arrhythmia database, and the MIMIC-III Waveform database, which comprises authentic ICU recordings of ECG, PPG, and blood pressure data from more than 40,000 patients were employed to evaluate the model. It can be seen from the results that FAC-CNN outperformed the other models used in the experiment, including CNNs and LSTMs by 4-7%, obtaining a mean accuracy of 97.4%, an F1-score of 96.8%, and an AUC score of 0.985. Moreover, the scalability of the proposed algorithm for applications in remote healthcare settings improved, and the more effective training method contributed to time-savings for the training procedure, decreasing it by 18.6%.

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