Research on China's Healthcare Development Issues Based on the Grey Forecasting Model

This paper focuses on four major issues and provides in-depth analysis. For issue one, the three-spline interpolation method is used to fill missing data, and the IQR method and box plot are employed to detect and remove outliers. The growth trend of medical institutions in different regions is analyzed. For issue two, principal component analysis (PCA) is used to reduce the dimensionality of healthcare resource allocation indicators, with the applicability of the analysis determined by the KMO and Bartlett tests. The relationship between resource allocation and regions is analyzed using the variance explanation table and Spearman correlation coefficient, while Moran's I index reveals spatial distribution characteristics. For issue three, the ARIMA model is used to predict the number of medical institutions in various regions over the next five years, with data stationarity tests and parameter optimization conducted. The models predictive ability is assessed through MSE. For issue four, the Pearson correlation coefficient is used to analyze key factors influencing the number of medical institutions, such as population aging and GDP, and the grey forecasting model is applied to predict future trends. The study shows that population aging and regional development imbalance will continue to affect the number of medical institutions, and economic development is highly correlated with healthcare levels. The paper proposes strategies to optimize the healthcare system, expand medical insurance coverage, and promote technological innovation, emphasizing the importance of addressing public health systems and the aging challenge.