The challenges and benefits of public health in smart cities from a 4 M perspective

With the acceleration of urbanization, public health issues have become increasingly prominent in smart city construction, especially in the face of sudden public health crises. A deep research method for public health management based on a 4M perspective (human, machine, materials, methods) is proposed to effectively address these challenges. Methods: The method involves studying the impact of human factors such as population age, gender, and occupation on public health from a human perspective. It incorporates a machine perspective by constructing a public health prediction model using deep neural networks. Additionally, it analyzes resource allocation and process optimization in public health management from the materials and methods perspectives.

The experiments demonstrate that the public health prediction model based on deep neural networks achieved a prediction accuracy of 98.6% and a recall rate of 97.5% on the test dataset. In terms of resource allocation and process optimization, reasonable adjustments and optimizations increased the coverage of public health services by 20% and decreased the response time to public health events by 30%.

This research method has significant benefits for addressing the challenges of public health in smart cities. It can improve the efficiency and effectiveness of public health services, helping smart cities respond more quickly and accurately to potential large-scale public health events in the future. This approach holds important theoretical and practical significance.