Research Article
Identification of Economic Factors for Mass Depression Based on Panel Study and Machine Learning
@INPROCEEDINGS{10.1007/978-3-031-65126-7_7, author={Iaroslava Pravolamskaya and Jian Chen and Wei Wang}, title={Identification of Economic Factors for Mass Depression Based on Panel Study and Machine Learning}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 19th EAI International Conference, QShine 2023, Shenzhen, China, October 8 -- 9, 2023, Proceedings, Part I}, proceedings_a={QSHINE}, year={2024}, month={8}, keywords={Panel Study Machine Learning Depression Identification Economic Factors Econometrics Models}, doi={10.1007/978-3-031-65126-7_7} }- Iaroslava Pravolamskaya
Jian Chen
Wei Wang
Year: 2024
Identification of Economic Factors for Mass Depression Based on Panel Study and Machine Learning
QSHINE
Springer
DOI: 10.1007/978-3-031-65126-7_7
Abstract
Panel study and machine learning are important tools for analyzing various aspects of the economy. They allow researchers to study the dynamics of changes in different economic indicators, such as GDP, inflation, unemployment, etc. In addition, these tools can be used to determine causal relationships between social, economic and psychological factors what can allow us to predict the development of the economy and changes in people’s life in the future. However, previous works in this sphere studied the connections between income and happiness, not taking into account the relationships between economic indicators and mental disorders. This article is aimed to analyze the relationship between economic factors and the level of mass depression based on a panel study and machine learning methods. Experimental results based on panel study and machine learning demonstrate effectiveness of our proposed econometric model.
