The rising global prevalence of diabetes necessitates early detection to prevent severe complications. While AI-powered prediction applications offer a promising solution, they require a responsive and scalable back-end architecture to serve a large user base effectively. This paper details the development and evaluation of a scalable back-end system designed for a mobile diabetes prediction application. The primary objective was to maintain a failure rate below 5% and an average latency of under 1000 ms. The architecture leverages horizontal scaling, database sharding, and asynchronous communication via a message queue. Performance evaluation showed that 83% of the system's features (20 out of 24) met the specified performance targets. Key functionalities such as user profile management, activity tracking, and read-intensive prediction operations successfully achieved the desired performance. The system demonstrated the ability to handle up to 10,000 concurrent users without issues, validating its scalability. The implementation of asynchronous communication using RabbitMQ proved crucial in minimizing the error rate for computationally intensive prediction requests, ensuring system reliability by queuing requests and preventing data loss under heavy load.

翻译：全球糖尿病患病率的不断上升使得早期检测成为预防严重并发症的必要手段。虽然基于人工智能的预测应用提供了一种有前景的解决方案，但它们需要一个响应迅速且可扩展的后端架构，以有效地服务大规模用户群。本文详细阐述了一款为移动糖尿病预测应用设计的可扩展后端系统的开发与评估。主要目标是将故障率维持在5%以下，并将平均延迟控制在1000毫秒以内。该架构利用了水平扩展、数据库分片以及通过消息队列实现的异步通信。性能评估表明，系统83%的功能（24项中的20项）达到了指定的性能目标。关键功能，如用户档案管理、活动跟踪和读密集型预测操作，均成功实现了预期的性能。该系统展示了能够处理多达10,000名并发用户而无问题的能力，验证了其可扩展性。使用RabbitMQ实现异步通信被证明对于最小化计算密集型预测请求的错误率至关重要，它通过排队请求并在高负载下防止数据丢失，确保了系统的可靠性。