Risk assessment system for respiratory medicine diseases based on machine learning

Abstract

A risk assessment system for respiratory medicine diseases based on machine learning, aiming to solve problems such as low accuracy and poor efficiency of traditional assessment methods. The system comprises a data layer, a model layer, and an application layer, and adopts a full-process architecture of "data acquisition-preprocessing-modeling-deployment". The data layer integrates multi-source data and constructs a standardized dataset; the model layer adopts a multi-algorithm fusion strategy, including a base model, a core model, and a time-series processing model, paired with a parameter optimization module and a performance evaluation module; the application layer includes modules such as an interactive interface and a report generation module, employs containerized deployment and a security management module. The system can improve assessment accuracy and efficiency, ensure medical data security, facilitate early clinical intervention, and has good clinical applicability.