Diagnosing and tracking depression based on eye movement in response to virtual reality.

ABSTRACT:

Introduction

Depression is a prevalent mental illness that is primarily diagnosed using psychological and behavioral assessments. However, these assessments lack objective and quantitative indices, making rapid and objective detection challenging. In this study, we propose a novel method for depression detection based on eye movement data captured in response to virtual reality (VR).

Methods

Eye movement data was collected and used to establish high-performance classification and prediction models. Four machine learning algorithms, namely eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP), Support Vector Machine (SVM), and Random Forest, were employed. The models were evaluated using five-fold cross-validation, and performance metrics including accuracy, precis

SUBMITTER: Zheng Z 

PROVIDER: S-EPMC10875075 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature