TITLE:
Study on the Analysis of Differentiated Demand for Citizen Healthcare Services Based on Data Mining
AUTHORS:
Hao Wan, Yihao Yin, Xin Li, Jing Yang
KEYWORDS:
Data Mining, Healthcare Services, Differentiated Demand, Random Forests
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.12 No.7,
July
29,
2024
ABSTRACT: This study uses data mining techniques to deeply analyze the demand for healthcare services of citizens of different age groups in Tianjin to provide reliable data support for improving the quality and efficiency of healthcare services. By collecting data from a wide range of samples, including residents of different age groups and different social backgrounds, we applied advanced methods such as random forests to conduct a comprehensive analysis. It was found that there were significant differences in the demand for healthcare services among different age groups and social groups. Specifically, age, education level, and health status significantly impact the demand for healthcare services. Older age groups may be more inclined to seek long-term care and chronic disease management services, while younger people may be more concerned about emergency medical treatment and preventive care. In addition, our study found that more educated people may be more focused on the level of healthcare technology and service experience, while individuals with poorer health status may have a more urgent need for healthcare resources. These findings provide valuable references for healthcare organizations and policymakers, helping them to meet the healthcare service needs of different groups more accurately, improve the efficiency of healthcare resource utilization, and achieve the goal of health for all. Future research can further explore the mechanisms by which different factors influence the demand for healthcare services and make targeted policy recommendations to promote the continuous optimization and innovation of the healthcare service system.