Optimization and Application of Cloud-based Deep Learning Architecture for Multi-Source Data Prediction

• Main Authors: Zhang, Yang, Wang, Fa, Huang, Xin, Li, Xintao, Liu, Sibei, Zhang, Hansong
• Format: Journal Article
• Language: English
• Published: 16-10-2024
• Subjects: Computer Science - Databases; Computer Science - Distributed, Parallel, and Cluster Computing; Computer Science - Learning; Quantitative Biology - Quantitative Methods
• DOI: 10.48550/arxiv.2410.12642

This study develops a cloud-based deep learning system for early prediction
of diabetes, leveraging the distributed computing capabilities of the AWS cloud
platform and deep learning technologies to achieve efficient and accurate risk
assessment. The system utilizes EC2 p3.8xlarge GPU instances to accelerate
model training, reducing training time by 93.2% while maintaining a prediction
accuracy of 94.2%. With an automated data processing and model training
pipeline built using Apache Airflow, the system can complete end-to-end updates
within 18.7 hours. In clinical applications, the system demonstrates a
prediction accuracy of 89.8%, sensitivity of 92.3%, and specificity of 95.1%.
Early interventions based on predictions lead to a 37.5% reduction in diabetes
incidence among the target population. The system's high performance and
scalability provide strong support for large-scale diabetes prevention and
management, showcasing significant public health value.