Artificial Neural Network Prediction of Psychological Well-Being Among Women Using Emotional, Cognitive, and Social Features
Keywords:
Psychological Well-Being, Artificial Neural Network, Self-Efficacy, Loneliness, Cognitive Flexibility, Social Support, Women, Machine LearningAbstract
Objective: This study aimed to develop and evaluate an artificial neural network model for predicting psychological well-being among women in Canada based on emotional, cognitive, and social features.
Methods and Materials: This cross-sectional predictive study was conducted among 428 adult women residing in Canada. Participants were recruited through community, academic, workplace, and online networks. Psychological well-being was assessed using Ryff’s Psychological Well-Being Scale. Emotional predictors included cognitive reappraisal, expressive suppression, and emotional intelligence; cognitive predictors included cognitive flexibility, self-efficacy, and mindfulness; and social predictors included perceived social support, social connectedness, and loneliness. Pearson correlations were used to examine bivariate relationships among variables. A multilayer perceptron artificial neural network with two hidden layers was developed using nine standardized input features. The dataset was randomly divided into training, validation, and testing subsets of approximately 70%, 15%, and 15%, respectively. Model performance was evaluated using mean squared error, root mean squared error, mean absolute error, coefficient of determination, and correlations between observed and predicted scores.
Findings: Psychological well-being was positively associated with self-efficacy, cognitive flexibility, perceived social support, emotional intelligence, social connectedness, mindfulness, and cognitive reappraisal, and negatively associated with loneliness and expressive suppression (all p < .001). The ANN demonstrated strong predictive performance, explaining 78% of variance in the training set, 73% in the validation set, and 71% in the independent testing set. In the test sample, RMSE was 5.78, MAE was 4.61, and the correlation between observed and predicted scores was .85. Self-efficacy showed the highest normalized importance, followed by loneliness, cognitive flexibility, perceived social support, and emotional intelligence. Cognitive and social domains contributed 40.20% and 40.60% of total predictive importance, respectively.
Conclusion: Psychological well-being among women can be predicted with substantial accuracy using an integrated set of emotional, cognitive, and social features, with self-efficacy, loneliness, cognitive flexibility, and social support emerging as particularly influential predictors.
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