Lin Yangyang, Zhou Dongqin
School of Smart Health Care, Zhejiang Dongfang Polytechnic, Wenzhou, China.
Nursing Teaching and Research Department, Wenzhou People's Hospital, Wenzhou, China.
Front Public Health. 2025 Apr 14;13:1571522. doi: 10.3389/fpubh.2025.1571522. eCollection 2025.
Postpartum 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.
This 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.
The 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.
The 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.
产后抑郁症（PPD）对新妈妈的家庭以及整个社会都有诸多不利影响。早期识别和干预具有重要意义。尽管现有许多用于PPD预测的机器学习分类器，但对高精度和模型可解释性的要求带来了新的挑战。
本文设计了一种用于预测PPD的集成神经网络模型，该模型将全连接神经网络（FCNN）和带有随机失活机制的神经网络（DNN）相结合。所提出模型中FCNN和DNN的权重由它们在训练集上的准确率和各自的随机失活值确定。FCNN的结构简单明了。FCNN神经元之间的连接模式使其易于理解特征与目标特征之间的关系，赋予所提出的模型可解释性。此外，所提出的模型不直接依赖随机失活机制来防止过拟合。其结构比DNN更稳定，削弱了随机失活机制对所提出模型可解释性的负面影响。同时，DNN的随机失活机制降低了所提出模型的过拟合风险，增强了其泛化能力，使所提出的模型能够更好地适应不同的临床数据。
所提出的模型在PPD数据集上实现了以下性能指标：准确率为0.933，精确率为0.958，召回率为0.939，F1分数为0.948，马修斯相关系数（MCC）为0.855，特异性为0.923，阴性预测值（NPV）为0.889，假阳性率（FPR）为0.077，假阴性率（FNR）为0.061。与10个经典机器学习分类器相比，在不同的数据集分割比例下，所提出的模型在准确率、精确率、召回率和F1分数等指标方面表现更优，并且具有较高的稳定性。
研究结果表明，所提出的模型有效提高了PPD的预测性能，可为相关医护人员和产后女性的临床决策提供指导建议。未来的计划包括收集更多疾病数据集，使用所提出的模型对这些疾病进行预测，并构建一个在线疾病预测平台来嵌入所提出的模型，这将有助于进行实时疾病预测。
