HealthEdgeAI: GAI and XAI Based Healthcare System for Sustainable Edge AI and Cloud Computing Environments
Concurrency and Computation: Practice and Experience, cilt.37, sa.9-11, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 37 Sayı: 9-11
- Basım Tarihi: 2025
- Doi Numarası: 10.1002/cpe.70057
- Dergi Adı: Concurrency and Computation: Practice and Experience
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, zbMATH
- Anahtar Kelimeler: edge AI, edge computing, explainable AI, generative AI, heart disease, sustainable cloud computing
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Coronary heart disease is a leading cause of mortality worldwide. Although no cure exists for this condition, appropriate treatment and timely intervention can effectively manage its symptoms and reduce the risk of complications such as heart attacks. Prior studies have mostly relied on a limited dataset from the UC Irvine Machine Learning Repository, predominantly focusing on Machine Learning (ML) models without incorporating Explainable Artificial Intelligence (XAI) or Generative Artificial Intelligence (GAI) techniques for dataset enhancement. While some research has explored cloud-based deployments, the implementation of edge AI in this domain remains largely under-explored. Therefore, this paper proposes HealthEdgeAI, a sustainable approach to heart disease prediction that enhances XAI through GAI-driven data augmentation. In our research, we assessed multiple AI models by evaluating accuracy, precision, recall, F1-score, and area under the curve (AUC). We also developed a web application using Streamlit to demonstrate our XAI methods and employed FastAPI to serve the optimal model as an API. Additionally, we examined the performance of these models in cloud computing and edge AI settings by comparing key Quality of Service (QoS) parameters, such as average response rate and throughput. To highlight the potential of sustainable edge AI and cloud computing, we tested edge devices with both low- and high-end configurations to illustrate differences in QoS. Ultimately, this study identifies current limitations and outlines prospective directions for future research in AI-based cloud and edge computing environments.