Explainable machine learning methods to predict postpartum depression risk

Postpartum depression (PPD) is a type of depression that mothers have following childbirth due to hormonal changes, psychological transition to parenting, and exhaustion. This depression strikes either during/or in the first year following childbirth. It is also a frequently disregarded medical condition that must be treated right away as it might have major repercussions. Machine learning (ML) and artificial intelligence (AI) are tools that healthcare professionals can utilize to anticipate this condition more rapidly and correctly. Consequently, we have demonstrated how to use explainable artificial intelligence (XAI) methods and heterogeneous classifiers to predict postpartum depression in mothers who have recently given birth. The K-Nearest Neighbor (KNN) model and the customized stack model outperformed all other classifiers. KNN model obtained 97% accuracy, 98% recall, and 95% precision and the stack model obtained 97% accuracy, 100% recall, and 94% precision, respectively. A set of frameworks and resources known as explainable artificial intelligence (XAI) facilitates the comprehension and interpretation of predictions made by machine learning algorithms. Four distinct XAI techniques: ELI5, Shapley Additive Values (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchor – have been used to interpret the model predictions. Explainability, interpretability, accountability, and transparency are crucial parameters of XAI, ensuring that machine learning models provide understandable and trustworthy results to users and stakeholders. The goal of this interdisciplinary research is to develop an automated diagnosis framework with tools that can transform therapy for postpartum depression leading to suicide attempts and empower medical professionals to offer mothers individualized, high-quality care.