Predicting Fertility Intentions Using Cultural Norms, Economic Security, and Relationship Satisfaction via a Machine Learning Approach
Keywords:
Fertility intentions, cultural norms, economic security, relationship satisfaction, machine learning, predictive modelingAbstract
Objective: The present study aimed to predict fertility intentions based on cultural norms, economic security, and relationship satisfaction using advanced machine learning techniques.
Methods and Materials: This study employed a cross-sectional, predictive-correlational design conducted on 512 adults of reproductive age in South Africa selected through stratified random sampling. Data were collected using standardized instruments including the Cultural Values Scale, Economic Stability Index, Dyadic Adjustment Scale, and a Fertility Intention Scale, all of which demonstrated acceptable validity and reliability in previous studies. Data were analyzed using IBM SPSS-27 for descriptive and correlational analyses, followed by machine learning modeling in Python using Random Forest, Support Vector Machine, Gradient Boosting, and Artificial Neural Network algorithms. Data preprocessing included normalization, missing data imputation, and categorical encoding. Model performance was evaluated using 10-fold cross-validation and metrics including accuracy, precision, recall, F1-score, and area under the ROC curve.
Findings: The results indicated significant positive relationships between cultural norms, economic security, relationship satisfaction, and fertility intentions (p < .01). Among predictors, economic security demonstrated the strongest predictive power, followed by relationship satisfaction and cultural norms. Machine learning results showed that the Artificial Neural Network achieved the highest performance (accuracy = 0.868, AUC = 0.926), followed by Gradient Boosting (accuracy = 0.856, AUC = 0.914) and Random Forest (accuracy = 0.842, AUC = 0.901), while Support Vector Machine showed comparatively lower performance (accuracy = 0.801, AUC = 0.862). Feature importance analysis confirmed the dominant role of economic security across all models.
Conclusion: The superior performance of machine learning models demonstrates their effectiveness in capturing complex, nonlinear relationships among predictors, offering a powerful approach for understanding and predicting reproductive decision-making.
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