Machine Learning Models for Early Detection of Couples at Risk for Emotional Disengagement

Abstract

The objective of this study is to develop and validate machine learning models capable of accurately identifying couples at early risk for emotional disengagement based on psychological, behavioral, and communication-related indicators. This predictive cross-sectional study included 156 couples (N = 312 individuals) from across Canada who completed validated measures assessing emotional disengagement, relationship satisfaction, conflict frequency, attachment dimensions, daily supportive behaviors, and communication responsiveness. Data were collected through secure online surveys, and dyadic datasets were prepared using actor–partner structures to preserve relational interdependence. Five machine learning algorithms—logistic regression, random forest, gradient boosting, support vector classifier, and a neural network model—were trained on cleaned and normalized datasets, with performance evaluated through accuracy, precision, recall, F1 score, and area under the ROC curve. Cross-validation procedures ensured the robustness and generalizability of the models. Inferential analyses demonstrated that gradient boosting achieved the highest predictive performance (AUC = 0.94), followed closely by neural networks (AUC = 0.93) and random forests (AUC = 0.91). All models significantly outperformed logistic regression and support vector classifiers, indicating the superiority of non-linear approaches for capturing subtle relational patterns. Emotional withdrawal was the strongest predictor across all models (p < .001), followed by daily supportive behaviors, communication responsiveness, attachment avoidance, and conflict frequency. Demographic variables showed no significant predictive contribution (all p > .05). Confusion matrix analyses revealed that gradient boosting produced the lowest false-negative rate, confirming its utility for early detection. The study demonstrates that machine learning models, particularly gradient boosting and neural networks, can accurately detect early signs of emotional disengagement in couples by integrating relational, emotional, and behavioral indicators. These findings highlight the potential of computational approaches to support preventive interventions and enhance clinical assessment in couple therapy.