The challenges and benefits of public health in smart cities from a 4 M perspective - Yuan, Lirong; Du, Lihong; Gao, Yonggang; Zhang, Yujin; Shen, Yongqing (Frontiers in Public Health, 2024)
With the acceleration of urbanization, public health issues are becoming increasingly prominent in the construction of smart cities, especially in the face of sudden public health crises.To more effectively address these challenges, a deep research method for public health management on the ground of a 4M perspective is proposed. From a human perspective, it studies the impact of factors such as population age, gender, and occupation on public health. It introduces a machine perspective and constructs a public health prediction model on the ground of deep neural networks. It conducts research from the perspectives of materials and methods, systematically analyzing resource allocation and process optimization in public health management. The experiment demonstrates that the public health prediction model on the ground of deep neural networks has 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, through reasonable adjustments and optimizations, the coverage of public health services has increased by 20%, while the response time to public health events has decreased by 30%. This research method has significant benefits in addressing the challenges of public health in smart cities. It can improve the efficiency and effectiveness of public health services, and help smart cities respond more quickly and accurately to potential large-scale public health events in the future. This has important theoretical and practical significance.