Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.

ABSTRACT:

Background

Previous studies have classified major depression and healthy control groups based on vocal acoustic features, but the classification accuracy needs to be improved. Therefore, this study utilized deep learning methods to construct classification and prediction models for major depression and healthy control groups.

Methods

120 participants aged 16-25 participated in this study, included 64 MDD group and 56 HC group. We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. In addition, we used Python for correlation analysis, and neural network to establish the model to distinguish whether participants experienced depression, predict the total depression score, and evaluate the effectiveness of the class

SUBMITTER: Liang L 

PROVIDER: S-EPMC11442283 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature