Machine learning-based prediction and risk factor analysis of depression among reproductive-aged women in Bangladesh:...

BackgroundDepression is a widespread mental health disorder that disproportionately affects women of reproductive age due to a combination of biological, social, and environmental factors. It significantly impacts productivity, increases morbidity and disability, and poses challenges to the global economy. In Bangladesh, there have been few studies addressing this issue using modern analytical methods, despite its importance for public health.ObjectivesThe study aims to develop the best predictive model for depression risk factor analysis and to assess the PHQ-9 scale.DesignThis study extracted data from the cross-sectional survey.MethodsWe utilized data from the BDHS 2022, which gathered information on depression using the Patient Health Questionnaire (PHQ-9). The study included 13,113 ever-married women aged 15-49 years. To develop the predictive model, several machine learning algorithms were used. The performance of each model was assessed using metrics such as accuracy, precision, recall, and specificity. SHapley Additive exPlanations (SHAP) analysis was conducted to interpret and rank each feature's contribution to the model's output.ResultsApproximately 4.54% of women experienced moderate to severe depression. The Boruta algorithm identified 21 significant risk factors from a total of 25 variables, spanning demographic, socioeconomic, household, and reproductive domains, for predicting depressive symptoms. The Random Forest (RF) and Decision Tree models showed good performance across different performance metrics, achieving sensitivity of (0.068, 95% CI:0.064-0.072) and (0.409, 95% CI:0.395-0.423), specificity of (0.946, 95% CI:0.945-0.948) and (0.640, 95% CI: 0.629-0.651), and accuracy of (0.906, 95% CI:0.905-0.907), and (0.630, 95% CI:0.620-0.641). Whereas, boosting models also showed comparable performance. SHAP analysis revealed that household size, number of children under 5 in the household, and number of women in the household were the most influential predictors.ConclusionThe study demonstrated the effectiveness of the RF and decision tree model in detecting depression among Bangladeshi women, proving to be a valuable tool for identifying and predicting risk factors related to women's mental health. The findings indicate that combining machine learning with the PHQ-9 would help screen for depressive symptoms in large-scale public health settings while accounting for different covariate effects.