Hybrid Use of Structural Equation Modeling and Machine Learning: Literature Review and Future Potential
DOI:
https://doi.org/10.5281/zenodo.15740696Keywords:
Structural equation modeling, Machine learning, Hybrit modellingAbstract
The aim of this paper is to comprehensively review the basic concepts of structural equation modeling (SEM) and machine learning, their application areas in the literature, and hybrid studies where they are used together. While SEM provides a robust theoretical framework for analyzing complex relationships, machine learning is notable for its ability to discover patterns from large data sets. The integration of the two methods allows for more in-depth analyses and stronger predictions in a wide range of fields from social sciences to healthcare. In this context, the review highlights the contributions and future potential of the combination of SEM and machine learning to research processes.
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