Predicting Family Stability Using Socioeconomic and Cultural Variables via Machine Learning
Keywords:
Family stability, socioeconomic status, cultural values, machine learning, predictive modeling, family cohesionAbstract
Objective: The present study aimed to predict family stability based on socioeconomic and cultural variables using advanced machine learning models.
Methods and Materials: This study employed a cross-sectional predictive-correlational design involving 428 participants from diverse urban and semi-urban regions of Ecuador selected through multistage cluster sampling. Data were collected using standardized instruments, including the Family Adaptability and Cohesion Evaluation Scale IV (FACES IV) to measure family stability and its subcomponents, alongside structured measures of socioeconomic status and culturally adapted scales assessing collectivism, traditionalism, gender role beliefs, and intergenerational norms. Data preprocessing included handling missing values, normalization, and categorical encoding. Machine learning algorithms, including Random Forest, Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, were implemented using Python-based analytical frameworks. Model performance was evaluated through 10-fold cross-validation, with accuracy, precision, recall, F1-score, and AUC-ROC as key performance indicators. Feature importance analysis was conducted to identify the most influential predictors of family stability.
Findings: The results indicated that Gradient Boosting achieved the highest predictive performance (AUC = 0.93; accuracy = 0.88), followed by Artificial Neural Networks (AUC = 0.92), Random Forest (AUC = 0.91), and Support Vector Machines (AUC = 0.88). Communication quality emerged as the strongest predictor of family stability, followed by socioeconomic status, family cohesion, and cultural collectivism. Significant positive associations were observed between family stability and communication (r = 0.67), socioeconomic status (r = 0.48), and collectivism (r = 0.42), indicating the combined influence of relational, structural, and cultural dimensions in shaping family outcomes.
Conclusion: The findings demonstrate that family stability is a multidimensional construct influenced by the interaction of socioeconomic conditions, cultural values, and relational processes, with machine learning models providing robust predictive capabilities. The prominence of communication quality highlights the critical role of intra-family dynamics, while the contribution of socioeconomic and cultural variables underscores the need for integrative and context-sensitive approaches in both research and intervention.
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