Prediction of postpartum depression in Zacatecas Mexico using a machine learning approach

Abstract

Postpartum depression (PPD) is a silent disorder, difficult to detect by the mother who suffers from it. In this research project, we propose a classification model of PPD using machine learning (ML) techniques, following a supervised learning approach. This is model allows the prediction of PPD using sociodemographic and medical data through a dataset of 100 Zacatecan mothers previously classified with the result of Edinburgh Test. We use eight ML algorithms such as adaptative boosting classified (ABC), principal component analysis (PCA) boosting, decision trees (DT), k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), and boosting. Our results show that the proposed ML model based on ABC algorithm can outperform other classifiers yielding a precision of 90%, a recall of 90%, a F1-score of 78% and 74% for area under curve (AUC), illustrating a correct capability in the prediction of this disorder.