Prediction of Postpartum Depression Using Machine Learning Techniques

Abstract

People post frequent updates on the social media platforms regarding their activities, likes and dislikes that adds the element of uniqueness and per- sonalization to the content. This study explores how data-driven methods can leverage the information available on social media platforms to predict Postpartum Depression (PPD). Early screening of mental disorders plays a crucial role in diagnosis and treatment. A generalized approach is proposed where linguistic features are extracted from user generated textual posts and categorized as general, depressive and PPD representative using multiple ma- chine learning techniques. We use Linguistic Inquiry Word Count (LIWC) to extract a standard set of features and combine it with an additional feature based on Absolutist dictionary before identifying a list of most important features for the task of prediction. We nd that the techniques used in our study exhibit strong predictive capabilities for PPD content. Multi-Layer Perceptron outperformed other techniques like SVM & Logistic Regression with 91.7% accuracy for depressive content identi cation and up to 86.9% accuracy for PPD content prediction. Our proposed methodology will help the government and humanitarian organizations to improve the systems and utilize available professional resources e ciently in order to deal with the situation of increasing occurrence of mental disorders.