Postpartum Depression Identification: Integrating Mutual Learning-based Artificial Bee Colony and Proximal Policy Optimization for Enhanced Diagnostic Precision

Postpartum depression (PPD) affects approximately 12% of mothers, posing significant challenges for maternal and child health. Despite its prevalence, many affected women lack adequate support. Early identification of those at high risk is cost-effective but remains challenging. This study introduces an innovative model for PPD detection, combining the Mutual Learning-based Artificial Bee Colony (ML-ABC) method with Proximal Policy Optimization (PPO). This model uses a PPO-based algorithm tailored to the imbalanced dataset characteristics, employing an artificial neural network (ANN) for policy formation in categorization tasks. PPO enhances stability by preventing drastic policy shifts during training, treating the training process as a series of interconnected decisions, with each data point considered a state. The network, acting as an agent, improves at recognizing fewer common classes through rewards or penalties. The model incorporates an advanced pre-training strategy using ML-ABC to adjust initial weight configurations to increase classification precision, enhancing early pattern recognition. Evaluated on a Swedish study (2009-2018) dataset comprising 4313 cases, the model demonstrates superior precision and accuracy, with accuracy and F-measure scores of 0.91 and 0.88, respectively, proving highly effective for identifying PPD.

@article{Tang2024,
  title     = {Postpartum Depression Identification: Integrating Mutual Learning-based Artificial Bee Colony and Proximal Policy Optimization for Enhanced Diagnostic Precision},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yayuan Tang and Tangsen Huang and Xiangdong Yin},
  doi       = {10.14569/IJACSA.2024.0150636},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150636}
}