Development of Software for the Analysis of Socio-Economic Indicators Based on Neural Networks

This research highlights the necessity of implementing neural networks in all areas of decision-making. In this case, we focus on developing a neural network for analyzing the effectiveness of decision-making in socio-economic systems, using the healthcare sector as an example. To prepare for development, it is essential to initially define the target indicator for forecasting and the set of factors influencing it. We have chosen four mortality indicators to evaluate across different socio-economic groups: infant mortality, mortality from tuberculosis, mortality from cardiovascular diseases, and mortality from neoplasms, including malignant ones. After collecting the initial data, the research describes the working principle and the development sequence of the Future Analytics program and presents the results of the analysis. The program is versatile in that it can analyze any data, but they must be closely related to the studied object.