Machine Learning Feature Importance for Detecting Early Warning Signs of Relationship Burnout in Couples

Abstract

This study aims to identify the most influential psychological, communicative, and digital-behavioral predictors of relationship burnout in couples using machine learning feature-importance analysis. A cross-sectional predictive design was implemented with a sample of 206 couples (412 individuals) from Turkey, collected through online recruitment. Participants completed validated measures assessing emotional exhaustion, communication avoidance, stress and emotional dysregulation, relational motivation, conflict behaviors, sexual and relational satisfaction, and digital interaction patterns such as shared online activity and response latency during conflictual exchanges. Data preprocessing included normalization, encoding, missing-value correction, and outlier management. Multiple machine learning models—random forests, gradient-boosted trees (XGBoost), multilayer perceptron networks, support vector machines, and logistic regression—were trained on a stratified 80/20 train–test split. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC. Feature importance was assessed using permutation importance, model-specific variable importance scores, and SHAP (SHapley Additive exPlanations) values to identify consistent early warning indicators. XGBoost achieved the highest predictive performance (Accuracy = 0.89, AUC = 0.94), followed by random forests (Accuracy = 0.86, AUC = 0.91). SHAP analysis revealed emotional exhaustion as the strongest predictor, followed by communication avoidance, response latency during conflict, emotional dysregulation, weekly conflict episodes, relational motivation, and digital disengagement ratio. Interaction effects showed that high emotional exhaustion combined with high communication avoidance produced a multiplicative increase in predicted burnout probability, confirming nonlinear relational deterioration patterns captured by the models. Machine learning modeling effectively identified early warning signs of relationship burnout, demonstrating that emotional, communicative, and digital-behavioral variables jointly predict relational decline. These findings highlight the need for integrating computational analytics into clinical screening and preventive relationship interventions.