Predicting Quality of Life Among Married Adults Using Family Cohesion, Work–Family Conflict, and Emotion Regulation: A Machine Learning Study

Seyed Ali Darbani; Leila Asadi

doi:10.61838/mpz16079

Authors

Seyed Ali Darbani Assistant Professor, Mehravar Marriage and Divorce Studies Research Department, Tehran, Iran Author https://orcid.org/0000-0002-2109-0741
Leila Asadi Master of Science in Clinical Psychology, Department of Psychology, CT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0001-7592-5524

DOI:

https://doi.org/10.61838/mpz16079

Keywords:

Quality of life, family cohesion, work–family conflict, emotion regulation, machine learning, married adults

Abstract

Objective: This study aimed to predict quality of life among married adults in Tehran using family cohesion, work–family conflict, and emotion-regulation strategies through comparative and interpretable machine learning models.

Methods and Materials: This cross-sectional predictive-correlational study was conducted among 420 married and employed adults residing in Tehran, Iran. Participants were recruited through multistage sampling from community centers, healthcare centers, public organizations, private companies, and self-employment settings across different geographical regions of the city. Data were collected using the World Health Organization Quality of Life–Brief Version, the cohesion subscale of the Family Adaptability and Cohesion Evaluation Scales III, the Multidimensional Work–Family Conflict Scale, and the Emotion Regulation Questionnaire. The dataset was divided into an 80% training set and a 20% independent test set. Elastic net regression, support vector regression, random forest regression, gradient boosting regression, extreme gradient boosting, and a multilayer perceptron were developed and compared using repeated 10-fold cross-validation. Model performance was evaluated through the coefficient of determination, root mean squared error, and mean absolute error. Permutation importance and Shapley additive explanations were used to interpret the best-performing model.

Findings: Extreme gradient boosting achieved the highest predictive accuracy, explaining 61% of the variance in quality of life in the independent test set, with an RMSE of 7.32 and an MAE of 5.67. Family cohesion was the strongest positive predictor, followed by cognitive reappraisal and perceived economic status. Strain-based and time-based work interference with family were the strongest negative predictors, while expressive suppression also reduced predicted quality of life. Removing family cohesion or work–family conflict dimensions produced the largest declines in model performance. The nonlinear model outperformed elastic net regression, indicating the presence of interaction and threshold effects among the predictors.

Conclusion: Quality of life among married adults can be predicted with substantial accuracy by integrating family cohesion, work–family conflict, and emotion regulation, with relational support and occupational-family strain emerging as the most influential factors.

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