HEALTH ECONOMIC EVALUATION OF DIAGNOSTIC AND PROGNOSTIC PREDICTION MODELS. A SYSTEMATIC REVIEW

Research on economic system based on fuzzy set comprehensive evaluation model

The development of the economic system is affected by many factors, and the stability of the traditional economic analysis model is difficult to maintain. In order to explore the efficient and stable economic system evaluation and analysis model, based on machine learning ideas, this study uses rough set algorithm as the basic algorithm, and applies the related methods of rough set and catastrophe model theory to the evaluation of ecological economic development level. Moreover, this study reduces the redundant index of the index system and calculates the importance of the index after reduction. Based on the catastrophe set model, this study uses MATLAB software programming to comprehensively quantify the ecological economy, and finally divides the ecological economic grade. In addition, this study combines rough set theory with fuzzy mathematics, and initially establishes a two-branch fuzzy evaluation model. Finally, this study combines the actual situation to use the established model to evaluate the regional eco-economic system. The research results show that the method proposed in this paper has a certain effect, which can provide a reference for subsequent related research.

Modeling of the ecological economic activity based on machine learning

The eco-economic activity modeling is an effective method to analyze the eco-economic system. From the existing models, it can be seen that the disadvantages of eco-economic activity modeling are that the model evaluation accuracy is not high, and the system stability is poor. In order to improve the evaluation effect of the ecological economic activity, based on the machine learning algorithm, this study establishes a PNN evaluation model based on the probabilistic neural network classification principle. Moreover, in this study, a certain number of learning samples are generated by random interpolation of evaluation index standards, and then Matlab software is used to simulate the training and test of the model, and the feasibility and effectiveness of the model are verified by statistical indicators. In addition, this study combines the actual case to analyze the performance of the model and analyze the test results by statistical analysis methods. The research results show that the model proposed in this study has certain effects and high stability.

Simulation analysis of financial stock market based on machine learning and GARCH model

With the rapid development of the world’s financial industry, the complexity and relevance of risks are gradually increasing. At present, there are still some deficiencies in the model for measuring financial risk. In view of this, this study analyzes the financial stock market and combines VAR model and GARCH model to conduct financial analysis. Moreover, this study uses the standard deviation in the statistical characteristics of the data to characterize the fluctuation of futures, and then uses the univariate GARCH model to measure the fluctuation. In addition, this study combines the examples to analyze the effectiveness of the model, and compares the predicted data with the actual data to verify the model performance. The results show that the algorithm proposed in this paper has certain effectiveness, and through this research algorithm, investors, speculators or macro decision makers in the futures market can obtain some inspiration.

Simulation of stock market investor behavior based on bayesian learning and complex network

The increasing complexity of the financial system has increased the uncertainty of the market, which has led to the complexity of the evolution of limited rational investor behavior decisions. Moreover, it also has a negative effect on the market and affects the development of the real economy and social stability. In view of the interconnected characteristics of various elements presented in financial complexity, based on complex network theory, Bayesian learning theory and social learning theory, this study systematically describes the behavioral decision-making mechanism of individual investors and institutional investors from the perspective of network learning. In addition, this study builds an evolutionary model of investor behavior based on Bayesian learning strategies. According to the results of the horizontal and vertical bidirectional studies simulated by experiments, we can see that the method proposed in this study has a certain effect on the evaluation and decision support of stock market investment.