Modeling the Investment Efficiency and Risk Assessment of Great Health Industry in the Context of Digital Economy

Abstract

Abstract With the development of the economy and society, people pay more and more attention to physical health. In order to make the big health enterprises in the long term at the level of smooth development, it is necessary to carry out an in-depth study on the investment efficiency and potential risk of the big health industry. This study constructs an investment efficiency evaluation method based on the DEA model. Firstly, the comprehensive efficiency is decomposed through the CCR model to further obtain the output results. Then, the effectiveness of enterprise investment is evaluated. The changes in the investment efficiency of the big health industry and other sample decision-making units are analyzed through the DEA-Malmquist model to output the trend of the overall investment efficiency. Logistic regression, support vector machine, and random forest models are used to assess the risk of the large health industry, respectively, and several classifiers are trained. When predicting the final sample, the voting or mean value method is used to count the effect of classification. The overall mean value of big health enterprises hovered between 0.96 and 0.98 in five years, indicating that the comprehensive investment efficiency of the big health industry is relatively stable. The average AUC value of the random forest model is 0.635, which is 0.028 higher than the average AUC value of the support vector machine; thus, it is concluded that there is no great fluctuation in the investment efficiency of the big health industry under the background of the digital economy, and the random forest model is more suitable for the risk assessment of the big health industry.