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Department of Psychology, Faculty of Humanities, Shahed University, Tehran, Iran. , shairigm@gmail.com
Abstract: (11 Views)
Background: Postpartum depression is one of the growing mental health problems among women, with widespread consequences for both the mother and the family. Previous studies have mostly focused on a limited range of variables and have used traditional analytical methods. However, multidimensional prediction using machine learning algorithms has received less attention.
Aims: The aim of this study was to predict postpartum depression based on multiple psychological, demographic, lifestyle, obstetric, and gynecological variables using a machine learning algorithm.
Methods: This research employed a correlational design based on machine learning algorithms. The statistical population included pregnant women from the 34th week of pregnancy onwards. A total of 198 participants were selected using convenience sampling. During the last month of pregnancy, they completed the Edinburgh Postnatal Depression Scale (EPDS; 2003), the Pregnancy Anxiety Questionnaire (Vandenberg, 1989), the Pittsburgh Sleep Quality Index (1989), and a researcher-made questionnaire. After delivery, 150 women from the same group completed the Social Support Questionnaire (Sherbourne & Stewart, 1991), the EPDS (2003), and another researcher-made questionnaire. Descriptive characteristics of the data were analyzed using SPSS version 27, and machine learning analysis was applied to determine the possibility of predicting postpartum depression based on multiple variables.
Results: The results showed that the AUC coefficient for the performance of the proposed model in predicting postpartum depression was 0.72, indicating good model performance. The analysis suggested that postpartum depression can indeed be predicted using multiple variables through machine learning algorithms. Among the predictors, social support, pregnancy anxiety, number of pregnancies, number of children, and primiparity had the greatest contribution to the final prediction model.
Conclusion: Considering the high prevalence of postpartum depression and its wide-ranging consequences, the results of this study may be applied in the design of machine learning-based screening tools for the early identification of at-risk women. Moreover, the findings can serve as a guide for planning educational and supportive interventions to enhance awareness among families and health policymakers.
Aims: The aim of this study was to predict postpartum depression based on multiple psychological, demographic, lifestyle, obstetric, and gynecological variables using a machine learning algorithm.
Methods: This research employed a correlational design based on machine learning algorithms. The statistical population included pregnant women from the 34th week of pregnancy onwards. A total of 198 participants were selected using convenience sampling. During the last month of pregnancy, they completed the Edinburgh Postnatal Depression Scale (EPDS; 2003), the Pregnancy Anxiety Questionnaire (Vandenberg, 1989), the Pittsburgh Sleep Quality Index (1989), and a researcher-made questionnaire. After delivery, 150 women from the same group completed the Social Support Questionnaire (Sherbourne & Stewart, 1991), the EPDS (2003), and another researcher-made questionnaire. Descriptive characteristics of the data were analyzed using SPSS version 27, and machine learning analysis was applied to determine the possibility of predicting postpartum depression based on multiple variables.
Results: The results showed that the AUC coefficient for the performance of the proposed model in predicting postpartum depression was 0.72, indicating good model performance. The analysis suggested that postpartum depression can indeed be predicted using multiple variables through machine learning algorithms. Among the predictors, social support, pregnancy anxiety, number of pregnancies, number of children, and primiparity had the greatest contribution to the final prediction model.
Conclusion: Considering the high prevalence of postpartum depression and its wide-ranging consequences, the results of this study may be applied in the design of machine learning-based screening tools for the early identification of at-risk women. Moreover, the findings can serve as a guide for planning educational and supportive interventions to enhance awareness among families and health policymakers.
Article number: 11
Keywords: Postpartum depression, Pregnancy-related anxiety, Antenatal depression, Social support, Machine learning algorithm
Type of Study: Research |
Subject:
Special
Received: 2025/08/15 | Accepted: 2026/01/18 | Published: 2026/07/23
Received: 2025/08/15 | Accepted: 2026/01/18 | Published: 2026/07/23
References
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This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)