Scalable Back-End for an AI-Based Diabetes Prediction Application

TL;DR

This paper presents a scalable, reliable back-end architecture for a mobile AI-based diabetes prediction app, capable of supporting 10,000 users with low latency and failure rates through horizontal scaling, sharding, and asynchronous messaging.

Contribution

It introduces a scalable back-end system design that effectively manages high user loads and maintains performance targets for AI-powered diabetes prediction applications.

Findings

83% of features met performance targets

System supports up to 10,000 concurrent users

Asynchronous messaging reduces error rates

Abstract

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.