Original scientific paper

https://doi.org/10.17559/TV-20220124122948

Intelligent Diagnosis Approach for Depression Using Vocal Source Features

Yuan Gao ; South-Central Minzu University, School of Biomedical Engineering, Wuhan 430074, China
Yinan Xin ; South-Central Minzu University, School of Biomedical Engineering, Wuhan 430074, China
Li Zhang ; South-Central Minzu University, School of Biomedical Engineering, Wuhan 430074, China

Abstract

Depression is the most widely affecting of mental illnesses for public health concern. Although there are many treatments for depression, barriers to diagnosis still exist. The intelligent diagnosis relying on extraction of biomarkers provides reliable indicators of depression. This paper proposed a new method of machine learning diagnosis based on vocal source features. The short-term and long-term features were combined for classification and evaluation. The long-term features contained four important short-term features selected by decision trees, and the random forest algorithm and extreme gradient boosting algorithm were used for classification. The results showed that our method was feasible to classify the degree of depression, F1 scores and sensitivity of non-depression were better than traditional short-term features, long-term features, and deep learning approaches. Our study provides a useful tool for preventing and diagnosing early depression.

Keywords

depression; feature combination; intelligent diagnosis; random forest algorithm

Hrčak ID:

275315

URI

https://hrcak.srce.hr/275315

Publication date:

19.4.2022.

Visits: 1.147 *