Novel Meta Learning Approach for Detecting Postpartum Depression Disorder Using Questionnaire Data
Shazia Nasim, Ahmad Sami Al‐Shamayleh, Nisrean Thalji, Ali Raza, Laith Abualigah, Ahmed Ibrahim Alzahrani, Ayed Alwadain, Deema Mohammed Alsekait, Hazem Migdady, Diaa Salama AbdElminaam
Abstract
Postpartum depression (PPD) is becoming increasingly prevalent worldwide, often manifesting in new mothers due to a complex interplay of physical, behavioral, and emotional transformations post-childbirth. The primary aim of our research is to analyze the contributory factors leading to PPD, including familial, social, and other maternal health-related aspects, and to devise a predictive model that can accurately assess the risk of PPD. In this research, we analyzed a benchmark dataset of 1,503 entries gathered from a medical institution, where the data was compiled through questionnaires disseminated using a digital Google Forms platform. We deployed eleven advanced machine-learning algorithms for comparison. We proposed a novel MDKR model, a meta-learner designed to excel in predicting PPD. Questionnaire data is initially processed in the proposed MDKR through the decision tree, k-nearest classifier, and random forest models. Subsequently, the outputs from these models are fed into a meta-learner multi-layer perceptron for the final prediction. Compared to state-of-the-art studies, the proposed MDKR model surfaced as the most proficient, with an exemplary accuracy of 99% in detecting PPD. In addition, we have confirmed the performance using k-fold validation and tuning hyperparameters. In the comparative assessment of all the models concerning their ability to predict PPD risk levels, MDKR emerged as the superior model. This meta-learning model has significantly contributed to identifying pivotal factors influencing PPD, enhancing the predictive framework within maternal healthcare domains.