Automatic Model for Postpartum Depression Identification using Deep Reinforcement Learning and Differential Evolution Algorithm

Postpartum depression (PPD) affects approximately 12% of new mothers, posing a significant health concern for both the mother and child. However, many women with PPD do not receive proper care. Preventative interventions are more cost-effective for high-risk women, but identifying those at risk can be challenging. To address this problem, we present an automatic model for PPD using a deep reinforcement learning approach and a differential evolution (DE) algorithm for weight initialization. DE is known for its ability to search for global optima in high-dimensional spaces, making it a promising approach for weight initialization. The policy of the model is based on an artificial neural network (ANN), treating the categorization issue as a policymaking stage-by-stage process. The DE algorithm is used to acquire initial weight values, with the agent obtaining samples and performing classifications in each step. The habitat provides an award for every categorization activity, considering a greater award for identification of the minor category to encourage precise detection. By using a particular compensatory technique and an encouraging learning system, the operator eventually decides the most excellent method for achieving its goals. The model's efficiency is evaluated by analyzing a set of data acquired from the population-based BASIC study carried out in Uppsala, Sweden, which covers the period from 2009 to 2018 and consists of 4313 samples. The experiential results, identified by known analysis criteria, indicate that the sample achieved better precision and correctness, making it suitable for identifying PPD. The proposed model could have significant implications for identifying at-risk women and providing timely interventions to improve maternal and child health outcomes.