Machine Learning in Family Research: A Systematic Review
Machine Learning in Family Research: A Systematic Review

Summary/Abstract: Machine learning is a powerful tool for extracting meaningful patterns from large datasets and performing predictive modeling. In recent years, machine learning methods have been increasingly applied in family sciences, mental health, and educational research. This systematic review aims to evaluate how machine learning methods are used to understand the impact of family dynamics on individuals’ mental health, educational attainment, and behavioral outcomes. A comprehensive literature search was conducted in the Web of Science, PubMed, Scopus, Science Direct, Ulakbim, and TRDizin databases, and 11 studies meeting the PICOS criteria were analyzed. The reviewed studies indicate that machine learning algorithms provide strong predictions in areas such as domestic violence, depression, academic achievement, and children’s psychosocial development. In particular, Random Forest (RF), Support Vector Machines (SVM), deep learning, and natural language processing (NLP) methods have demonstrated high accuracy in predictive tasks. However, challenges related to model transparency, ethical concerns, and applicability within the family context remain among the limitations of machine learning models. Therefore, future research should focus on enhancing the interpretability of machine learning approaches, integrating them with theoretical models, and supporting their application in family sciences with more empirical studies. By doing so, machine learning techniques can be used more effectively to understand family dynamics and support individuals' mental health.

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