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.

翻译：本研究开发了一种基于云平台的深度学习系统，用于糖尿病的早期预测，利用AWS云平台的分布式计算能力和深度学习技术，实现高效准确的风险评估。该系统采用EC2 p3.8xlarge GPU实例加速模型训练，在保持94.2%预测准确率的同时，将训练时间减少了93.2%。通过使用Apache Airflow构建的自动化数据处理与模型训练流水线，系统可在18.7小时内完成端到端更新。在临床应用中，该系统展现出89.8%的预测准确率、92.3%的灵敏度和95.1%的特异性。基于预测结果的早期干预使目标人群的糖尿病发病率降低了37.5%。该系统的高性能与可扩展性为大规模糖尿病预防与管理提供了有力支持，展现出显著的公共卫生价值。