Machine Learning for the Prediction of Depression Progression from Inflammation Markers

Major depressive disorder is one of the major contributors to disability worldwide with an estimated prevalence of 4%. Depression is a heterogeneous disease often characterized by an undefined pathogenesis and multifactorial phenotype that complicate diagnosis and follow-up. Translational research and identification of objective biomarkers including inflammation can assist clinicians in diagnosing depression and disease progression. Investigating inflammation markers using machine learning methods combines recent understanding of the pathogenesis of depression associated with inflammatory changes as part of chronic disease progression that aims to highlight complex interactions. In this paper, 721 patients attending a diabetes health screening clinic (DiabHealth) were classified into no depression (none) to minimal depression (none-minimal), mild depression, and moderate to severe depression (moderate-severe) based on the Patient Health Questionnaire (PHQ-9). Logistic Regression, K-nearest Neighbors, Support Vector Machine, Random Forest, Multi-layer Perceptron, and Extreme Gradient Boosting were applied and compared to predict depression level from inflammatory marker data that included C-reactive protein (CRP), Interleukin (IL)-6, IL-1 beta, IL-10, Complement Component 5a (C5a), D-Dimer, Monocyte Chemoattractant Protein (MCP)-1, and Insulin-like Growth Factor (IGF)-1. MCP-1 and IL-1 beta were the most significant inflammatory markers for the classification performance of depression level. Extreme Gradient Boosting outperformed the models achieving the highest accuracy and Area Under the Receiver Operator Curve (AUC) of 0.89 and 0.95, respectively.