Identifying the Most Effective Predictors of Depression in Female University Students Using Feature Selection Techniques
Keywords:
Depression prediction, Feature selection, Student depression, Mental healthAbstract
Objective: The aim of this study was to identify the most effective predictors of depression in female university students using feature selection techniques and machine learning algorithms.
Methods and Materials: This correlational study was conducted among female students at the Islamic Azad University, Ahvaz Branch, during the second semester of the 2022–2023 academic year. A convenience sample of 411 students completed online standardized questionnaires including the Beck Depression Inventory-II (BDI-II), Emotional Intelligence, Rumination, Loneliness, Perfectionism, the Big Five Personality Inventory, and a Socioeconomic Status scale. Data were analyzed using a particle swarm optimization (PSO) algorithm for feature selection and simultaneous-entry multiple regression for hypothesis testing.
Findings: Feature selection analysis revealed that seven psychological variables—neuroticism, emotional intelligence, rumination, loneliness, perfectionism, agreeableness, and anxiety—constituted the optimal predictor set for depression in female students. The regression model showed that these variables accounted for 51% of the variance in depression (F = 32.48, p < 0.01). Among them, neuroticism and emotional intelligence had the most significant predictive effects.
Conclusion: The results indicated that a specific combination of personality traits and emotional variables can significantly predict depression in female university students. These findings provide a foundation for designing preventive and therapeutic interventions aimed at reducing depression in this population.
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References
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