Novel Meta Learning Approach for Detecting Postpartum Depression Disorder Using Questionnaire Data

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.