Prediction of postpartum depression in women: development and validation of multiple machine learning models
Abstract Background Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several risk assessment tools for the early detec...
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| Format: | Article |
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BMC
2025-03-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06289-6 |
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| author | Weijing Qi Yongjian Wang Yipeng Wang Sha Huang Cong Li Haoyu Jin Jinfan Zuo Xuefei Cui Ziqi Wei Qing Guo Jie Hu |
| author_facet | Weijing Qi Yongjian Wang Yipeng Wang Sha Huang Cong Li Haoyu Jin Jinfan Zuo Xuefei Cui Ziqi Wei Qing Guo Jie Hu |
| author_sort | Weijing Qi |
| collection | DOAJ |
| description | Abstract Background Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several risk assessment tools for the early detection of PPD. Methods Candidate predictors, including history of mental illness and demographic, psychosocial, and physiological factors, were obtained from 1138 perinatal women between August 2021 and August 2022. The primary outcome of PPD was measured with the Edinburgh Postnatal Depression Scale at 6 weeks postpartum. Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared. Results A total of 11 potential predictive factors associated with PPD were identified and subsequently used to construct prenatal and postpartum predictive models for PPD. The cross-validation results showed that the models built on logistic regression (LR) [area under the curve (AUC): 0.801, 0.858] and artificial neural network (ANN) (AUC: 0.787, 0.844) algorithms exhibited the best prediction performance. In contrast to the prenatal models, the addition of postpartum predictors (primary caregiver and mother-in-law’s care) remarkably improved the predictive performance of the postpartum models. The risk-stratification score, the nomogram, and the Shapley additive explanation were used to visualize and interpret the risk prediction model for predicting PPD in the early stage. Conclusions The LR and ANN models achieved the best predictive performances. Applying these models and risk assessment tools to early predict and screen PPD has several implications for public health. Graphical Abstract |
| format | Article |
| id | doaj-art-c1c5c47afd1844368dc5cc0c5004e1b3 |
| institution | DOAJ |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-c1c5c47afd1844368dc5cc0c5004e1b32025-08-20T02:59:57ZengBMCJournal of Translational Medicine1479-58762025-03-0123111810.1186/s12967-025-06289-6Prediction of postpartum depression in women: development and validation of multiple machine learning modelsWeijing Qi0Yongjian Wang1Yipeng Wang2Sha Huang3Cong Li4Haoyu Jin5Jinfan Zuo6Xuefei Cui7Ziqi Wei8Qing Guo9Jie Hu10Humanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityShijiazhuang Obstetrics and Gynecology HospitalHumanistic Care and Health Management Innovation Center, School of Nursing, Hebei Medical UniversityAbstract Background Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several risk assessment tools for the early detection of PPD. Methods Candidate predictors, including history of mental illness and demographic, psychosocial, and physiological factors, were obtained from 1138 perinatal women between August 2021 and August 2022. The primary outcome of PPD was measured with the Edinburgh Postnatal Depression Scale at 6 weeks postpartum. Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared. Results A total of 11 potential predictive factors associated with PPD were identified and subsequently used to construct prenatal and postpartum predictive models for PPD. The cross-validation results showed that the models built on logistic regression (LR) [area under the curve (AUC): 0.801, 0.858] and artificial neural network (ANN) (AUC: 0.787, 0.844) algorithms exhibited the best prediction performance. In contrast to the prenatal models, the addition of postpartum predictors (primary caregiver and mother-in-law’s care) remarkably improved the predictive performance of the postpartum models. The risk-stratification score, the nomogram, and the Shapley additive explanation were used to visualize and interpret the risk prediction model for predicting PPD in the early stage. Conclusions The LR and ANN models achieved the best predictive performances. Applying these models and risk assessment tools to early predict and screen PPD has several implications for public health. Graphical Abstracthttps://doi.org/10.1186/s12967-025-06289-6Postpartum depressionMachine learningPredictive factorsPrediction model |
| spellingShingle | Weijing Qi Yongjian Wang Yipeng Wang Sha Huang Cong Li Haoyu Jin Jinfan Zuo Xuefei Cui Ziqi Wei Qing Guo Jie Hu Prediction of postpartum depression in women: development and validation of multiple machine learning models Journal of Translational Medicine Postpartum depression Machine learning Predictive factors Prediction model |
| title | Prediction of postpartum depression in women: development and validation of multiple machine learning models |
| title_full | Prediction of postpartum depression in women: development and validation of multiple machine learning models |
| title_fullStr | Prediction of postpartum depression in women: development and validation of multiple machine learning models |
| title_full_unstemmed | Prediction of postpartum depression in women: development and validation of multiple machine learning models |
| title_short | Prediction of postpartum depression in women: development and validation of multiple machine learning models |
| title_sort | prediction of postpartum depression in women development and validation of multiple machine learning models |
| topic | Postpartum depression Machine learning Predictive factors Prediction model |
| url | https://doi.org/10.1186/s12967-025-06289-6 |
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