Depression Recognition Using Machine Learning Algorithms With Eye Tracking, Visual Evoked Potentials, and Auditory P300 Among Chinese Medical Students.

Background: Current assessment of depression primarily relies on psychological scales. Although the use of machine learning in depression has grown, limited reports are available on multiple neurophysiological measurements. We employed machine learning algorithms incorporating eye tracking, visual evoked potentials (VEPs), and auditory P300 to classify depression among Chinese medical students. Methods: A total of 66 students with depression and 72 matched controls were recruited; eye tracking, VEPs, and auditory P300 data were collected. Descriptive analyses and group comparisons were performed between the depression and control groups. Then, multivariate logistic regression (LR) analysis was conducted to evaluate the relationship between eye tracking, VEPs, and auditory P300 features and Patient Health Questionnaire-9 (PHQ-9) scores. Furthermore, the study employed six classifiers to differentiate between depression and nondepression. Five-fold cross-validation was employed. Model performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), precision, accuracy, recall, and F1 score. We applied SHapley Additive exPlanations (SHAP) values to explain the model. Results: Depression group was characterized by lower response search scores, higher Conclusions: Chinese medical students with depression exhibited reduced responsive search scores and extended P100 latencies, suggesting impairments in attention and visual information processing associated with depression. The combined eye tracking and VEPs proved to be more effective than single features for distinguishing depression and nondepression. P100 latency in the right eye may be the most significant predictor of depression.