Employing Cloud-Integrated Neural Network for Prediction of Heart Disease Using Advanced Telemedicine Solutions

Globally, cardiovascular disorders, including Atrial Fibrillation (AF), are a major health issue. Telemedicine and cloud-based neural networks are used to improve heart disease diagnosis and monitoring for AF patients in this research. Our technique uses remote patient monitoring and AI to improve AF diagnosis, treatment, and management. This paper develops a cloud-integrated neural network model to interpret real-time patient electrocardiogram (ECG) data. The neural network is trained on a broad dataset to detect modest AF start or progression patterns. Scalable, accessible, and effective data management is achieved using cloud computing. Telemedicine is crucial to this framework since it allows patients to be monitored remotely. Healthcare practitioners get notifications for abnormal ECG patterns, allowing quick intervention and individualized treatment. Patient data is safely saved in the cloud for long-term analysis and population-level AF research. The suggested telemedicine method improves AF event detection accuracy, according to preliminary data. Cloud-based neural networks improve diagnostics and enable healthcare research and information exchange. The system advances cardiac disease management, emphasizing AF, using integration and technology. This research may improve patient outcomes, lower healthcare costs, and increase population-level cardiovascular health knowledge. Teleconsultations maintain patient care while protecting sensitive health data. The system's flexibility and learning increase diagnostic accuracy.