Prediction of Depression Severity and Personalised Risk Factors Using Machine Learning on Multimodal Data

Depression is a widespread mental health issue with profound global impact, often leading to diminished life quality and increased suicide risk. Despite available treatments, many depression cases go unnoticed and untreated. This underscores the necessity for a precise, personalized model to predict depression severity and individual risk factors, utilizing machine learning on comprehensive, multimodal datasets. While previous efforts employing machine learning (ML) to gauge depression severity exist, their effectiveness has been curtailed by small datasets and a lack of personalization. To address this gap, we propose an advanced ML-based approach for predicting depression severity and identifying personalized risk factors. ML enhances the precision of depression severity assessments, facilitates personalized treatment strategies, and improves the identification of individual risk factors. In our study, we implemented, assessed, and compared five supervised ML algorithms—Linear Regression (LR), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO)—known for their accuracy, interpretability, and computational efficiency. We utilized a multimodal dataset from the National Health and Nutrition Examination Survey (NHANES), encompassing demographic, dietary, socio-economic, lifestyle, medical, laboratory, and clinical data. The Random Forest algorithm proved to be the most effective, demonstrating an R-squared of 0.93, an explained variance score (EVS) of 0.93, a mean absolute error (MAE) of 0.51, a mean squared error (MSE) of 1.73, and a root mean squared error (RMSE) of 1.32. It effectively pinpointed both general and personalized risk factors for depression severity. Our model not only proves effective in predicting depression severity and identifying personalized risk factors but also shows promise for clinical application in assessment, diagnosis, treatment planning, and depression management.