Machine Learning-Based Prediction of Depression In Women: A Scoping Review

Abstract

Background: Depression is a complex mental health disorder characterized by persistent feelings of sadness, loss of interest, and impaired daily functioning. Depression has been proven to affect women at a higher rate compared to men, with several studies providing evidence for this gender disparity. Fighting mental diseases like anxiety and depression especially in women has become a global concern. In the drive to find effective and efficient ways to combat this, machine learning techniques have been included in the healthcare systems for the possible diagnosis and prediction. Methods: Employing PRISMA-ScR guidelines, we employed a review to investigate the application of machine learning techniques to predict depression among women. A comprehensive review of five hundred and twenty peer-reviewed studies on depression from 2015 to 2025 were conducted and then narrowed down to sixteen that has to do with depression in women only. The review identifies several well-known models for forecasting depression, models like Decision tree, Naïve Bayes, Logistic Regression (LR), Artificial Neural Network (ANN), XGBoost, Random Forest (RF), Support Vector Machine (SVM).Results: Random Forest showed promising performance in the reviewed studies, but its superiority and clinical utility require independent validation. There are still need such as the requirement for larger and more varied datasets, the need to consider the diversity of mental health conditions, and the requirement to include longitudinal data to capture temporal dynamics.Discussion: This review while demonstrating the fact that machine learning models especially Random Forest show strong predictive capabilities for depression in women, data variety and gender-specific feature integration have a significant role to play. The result emphasized on the significance of multimodal data integration and to improve reliability as well as applicability, it is of note that future research pay attention to using larger datasets and gender sensitive modelling.Conclusion: The application of machine learning in the prediction of depression in women offers the potential forecasting detection, personalized interventions and enhanced outcome among the gender