Disease Prevention and Assistance System for Public Welfare Organizations Based on Lorentz-RR Analysis Technology

Health concerns have become a central focus in daily life. With the growing demand for human health monitoring, certain social organizations face an urgent need to strengthen health detection technologies, giving rise to various related techniques. This study aims to construct a disease prevention and assistance system for social organizations that integrates the strengths and resources of multiple parties — including social organizations, social service agencies, social workers, caregivers, service recipients, and their families. The main system adopts a front-end and back-end decoupled architectural design, underpinned by core technological innovation: namely, the Lorenz-RR scatter plot classification algorithm, encompassing classification algorithm selection, AlexNet algorithm development, and dataset expansion algorithm optimization, thereby completing the improvement of the AlexNet-based Lorenz-RR scatter plot classification algorithm and establishing the disease prevention and assistance system for social organizations. The system demonstrates high accuracy and high sensitivity in the analysis of service recipient indicators, offering substantial application value and broad potential for widespread adoption.