Classification of Depression Based on Functional Near-Infrared Spectroscopy (fNIRS) Signals Using Machine Learning Algorithms

Abstract

Depression is a significant mental health issue affecting individuals worldwide. In this study, we aimed to classify healthy, depressed, and suicidal individuals using functional nearinfrared spectroscopy (fNIRS) signals combined with machine learning algorithms. The dataset consisted of fNIRS measurements collected from participants in different mental states. Our experiment indicates that the implementation of the histogram based gradient boosting algorithm (HGBM) achieved the highest accuracy rate of 78.76% and the highest precision rate of 92% for depressed category. The HGBM outperformed other algorithms such as k-NN and CatBoosting. The study highlights the potential of fNIRS and machine learning in the detection and classification of depression.