AI-Based Prediction of Intimacy Decline from Dyadic Attachment and Stress Patterns
Keywords:
AI-Based, Intimacy, Dyadic Attachment, Stress PatternsAbstract
The objective of this study was to develop and evaluate an artificial intelligence–based model capable of predicting intimacy decline in romantic couples using dyadic attachment orientations and individual and shared stress patterns. The study employed a longitudinal, correlational design with a predictive modeling framework. Married and long-term cohabiting couples from Turkey participated as dyads, with both partners independently completing validated self-report measures of attachment anxiety and avoidance, perceived individual stress, dyadic stress, and relational intimacy. Data were collected at baseline and at a six-month follow-up to capture changes in intimacy over time. After data preprocessing and dyadic feature construction, multiple supervised machine learning algorithms were trained and validated using cross-validation procedures to predict intimacy decline as both a categorical and continuous outcome. The predictive models demonstrated strong performance, with ensemble-based algorithms achieving the highest classification accuracy and area under the curve values in distinguishing couples with declining intimacy from those with stable intimacy. Inferential feature analyses indicated that attachment anxiety, attachment avoidance, dyadic stress, and interaction terms between attachment insecurity and stress were the most influential predictors. Models incorporating dyadic discrepancy indicators consistently outperformed those based solely on individual-level features, indicating significant partner interdependence effects. Higher combined levels of attachment insecurity and stress were associated with greater magnitude of intimacy decline over time. The findings indicate that intimacy decline can be accurately predicted using AI-based models that integrate dyadic attachment and stress variables, supporting the view that intimacy erosion emerges from complex, interactive relational processes. These results highlight the potential of artificial intelligence to inform early identification and prevention strategies in couple and family interventions.
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Copyright (c) 2026 Büşra Kavla , Nurdan Sandıkçı (Author); Mehmet Yüksel
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