Predicting Divorce Risk Using Machine Learning: Integrating Emotional, Behavioral, and Relational Variables
Keywords:
Divorce risk, machine learning, emotional factors, behavioral factors, marital communication, predictionAbstract
This study aimed to develop and evaluate a machine learning model for predicting divorce risk by integrating emotional, behavioral, and relational variables among married couples in Tehran. A correlational–predictive design was conducted on 587 married couples recruited from family counseling centers in Tehran. Standardized measures of emotion regulation, marital satisfaction, conflict patterns, emotional intimacy, empathy, and relational indicators were administered. Divorce risk was operationalized using combined indices of marital dissatisfaction, separation intention, and clinical assessment. After preprocessing and normalization, data were split into training and testing sets, and logistic regression, support vector machine, random forest, gradient boosting, and artificial neural network models were trained. The artificial neural network achieved the highest predictive performance with 0.93 accuracy and 0.97 AUC. Feature importance analysis identified marital satisfaction, emotion regulation, destructive conflict, emotional intimacy, and empathy as the strongest predictors. Significant differences were observed between low-risk and high-risk couples across all emotional, behavioral, and relational variables (p < 0.001). Machine learning provides a powerful framework for early identification of couples at risk of divorce and offers a strong foundation for designing targeted preventive interventions in family counseling systems.
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