A method for predicting postpartum depression via an ensemble neural network model
IntroductionPostpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers for PPD prediction, the requirements for high accur...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1571522/full |
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| author | Yangyang Lin Dongqin Zhou |
| author_facet | Yangyang Lin Dongqin Zhou |
| author_sort | Yangyang Lin |
| collection | DOAJ |
| description | IntroductionPostpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers for PPD prediction, the requirements for high accuracy and the interpretability of models present new challenges.MethodsThis paper designs an ensemble neural network model for predicting PPD, which combines a Fully Connected Neural Network (FCNN) and a Neural Network with Dropout mechanism (DNN). The weights of FCNN and DNN in the proposed model are determined by their accuracies on the training set and respective Dropout values. The structure of the FCNN is simple and straightforward. The connection pattern among the neurons of the FCNN makes it easy to understand the relationship between the features and the target feature, endowing the proposed model with interpretability. Moreover, the proposed model does not directly rely on the Dropout mechanism to prevent overfitting. Its structure is more stable than that of the DNN, which weakens the negative impact of the Dropout mechanism on the interpretability of the proposed model. At the same time, the Dropout mechanism of the DNN reduces the overfitting risk of the proposed model and enhances its generalization ability, enabling the proposed model to better adapt to different clinical data.ResultsThe proposed model achieved the following performance metrics on the PPD dataset: accuracy of 0.933, precision of 0.958, recall of 0.939, F1-score of 0.948, Matthews Correlation Coefficient (MCC) of 0.855, specificity of 0.923, Negative Predictive Value (NPV) of 0.889, False Positive Rate (FPR) of 0.077, and False Negative Rate (FNR) of 0.061. Compared with 10 classic machine learning classifiers, under different dataset split ratios, the proposed model outperforms in terms of indicators such as accuracy, precision, recall, and F1-score, and also has high stability.DiscussionThe research results show that the proposed model effectively improves the prediction performance of PPD, which can provide guiding suggestions for relevant medical staff and postpartum women in clinical decision-making. In the future, plans include collecting more disease datasets, using the proposed model to predict these diseases, and constructing an online disease prediction platform to embed the proposed model, which will help with real-time disease prediction. |
| format | Article |
| id | doaj-art-944856c269b34834a510ca5f6bb0b237 |
| institution | DOAJ |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-944856c269b34834a510ca5f6bb0b2372025-08-20T03:06:56ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-04-011310.3389/fpubh.2025.15715221571522A method for predicting postpartum depression via an ensemble neural network modelYangyang Lin0Dongqin Zhou1School of Smart Health Care, Zhejiang Dongfang Polytechnic, Wenzhou, ChinaNursing Teaching and Research Department, Wenzhou People's Hospital, Wenzhou, ChinaIntroductionPostpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers for PPD prediction, the requirements for high accuracy and the interpretability of models present new challenges.MethodsThis paper designs an ensemble neural network model for predicting PPD, which combines a Fully Connected Neural Network (FCNN) and a Neural Network with Dropout mechanism (DNN). The weights of FCNN and DNN in the proposed model are determined by their accuracies on the training set and respective Dropout values. The structure of the FCNN is simple and straightforward. The connection pattern among the neurons of the FCNN makes it easy to understand the relationship between the features and the target feature, endowing the proposed model with interpretability. Moreover, the proposed model does not directly rely on the Dropout mechanism to prevent overfitting. Its structure is more stable than that of the DNN, which weakens the negative impact of the Dropout mechanism on the interpretability of the proposed model. At the same time, the Dropout mechanism of the DNN reduces the overfitting risk of the proposed model and enhances its generalization ability, enabling the proposed model to better adapt to different clinical data.ResultsThe proposed model achieved the following performance metrics on the PPD dataset: accuracy of 0.933, precision of 0.958, recall of 0.939, F1-score of 0.948, Matthews Correlation Coefficient (MCC) of 0.855, specificity of 0.923, Negative Predictive Value (NPV) of 0.889, False Positive Rate (FPR) of 0.077, and False Negative Rate (FNR) of 0.061. Compared with 10 classic machine learning classifiers, under different dataset split ratios, the proposed model outperforms in terms of indicators such as accuracy, precision, recall, and F1-score, and also has high stability.DiscussionThe research results show that the proposed model effectively improves the prediction performance of PPD, which can provide guiding suggestions for relevant medical staff and postpartum women in clinical decision-making. In the future, plans include collecting more disease datasets, using the proposed model to predict these diseases, and constructing an online disease prediction platform to embed the proposed model, which will help with real-time disease prediction.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1571522/fullpostpartum depressionneural networksmachine learningclinical decision-makingpostpartum women |
| spellingShingle | Yangyang Lin Dongqin Zhou A method for predicting postpartum depression via an ensemble neural network model Frontiers in Public Health postpartum depression neural networks machine learning clinical decision-making postpartum women |
| title | A method for predicting postpartum depression via an ensemble neural network model |
| title_full | A method for predicting postpartum depression via an ensemble neural network model |
| title_fullStr | A method for predicting postpartum depression via an ensemble neural network model |
| title_full_unstemmed | A method for predicting postpartum depression via an ensemble neural network model |
| title_short | A method for predicting postpartum depression via an ensemble neural network model |
| title_sort | method for predicting postpartum depression via an ensemble neural network model |
| topic | postpartum depression neural networks machine learning clinical decision-making postpartum women |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1571522/full |
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