Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(9-11)

Опубликована: Апрель 10, 2025

ABSTRACT 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 reduce the risk complications such as attacks. Prior studies have mostly relied on limited dataset from UC Irvine Machine Learning Repository, predominantly focusing (ML) models without incorporating Explainable Artificial Intelligence (XAI) or Generative (GAI) techniques enhancement. While some research has explored cloud‐based deployments, implementation edge AI in domain remains largely under‐explored. Therefore, paper proposes HealthEdgeAI , sustainable approach to prediction that enhances XAI through GAI‐driven data augmentation. In our research, we assessed multiple by evaluating accuracy, precision, recall, F1‐score, area under curve (AUC). We also developed web application using Streamlit demonstrate methods employed FastAPI serve optimal model an API. Additionally, examined performance these cloud computing settings comparing key Quality Service (QoS) parameters, average response rate throughput. To highlight potential computing, tested devices with both low‐ high‐end configurations illustrate differences QoS. Ultimately, study identifies current limitations outlines prospective directions future AI‐based environments.

Язык: Английский