Intelligent Diagnosis Approach for Depression Using Vocal Source Features
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/398891 |
| _version_ | 1797206702389460992 |
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| author | Yuan Gao Yinan Xin Li Zhang |
| author_facet | Yuan Gao Yinan Xin Li Zhang |
| author_sort | Yuan Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:11:13Z |
| format | Article |
| id | doaj.art-1fb8dfd1f42c473988063622ca9759ea |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:11:13Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-1fb8dfd1f42c473988063622ca9759ea2024-04-15T17:39:41ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-0129397197510.17559/TV-20220124122948Intelligent Diagnosis Approach for Depression Using Vocal Source FeaturesYuan Gao0Yinan Xin1Li Zhang2South-Central Minzu University, School of Biomedical Engineering, Wuhan 430074, ChinaSouth-Central Minzu University, School of Biomedical Engineering, Wuhan 430074, ChinaSouth-Central Minzu University, School of Biomedical Engineering, Wuhan 430074, ChinaDepression 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.https://hrcak.srce.hr/file/398891depressionfeature combinationintelligent diagnosisrandom forest algorithm |
| spellingShingle | Yuan Gao Yinan Xin Li Zhang Intelligent Diagnosis Approach for Depression Using Vocal Source Features Tehnički Vjesnik depression feature combination intelligent diagnosis random forest algorithm |
| title | Intelligent Diagnosis Approach for Depression Using Vocal Source Features |
| title_full | Intelligent Diagnosis Approach for Depression Using Vocal Source Features |
| title_fullStr | Intelligent Diagnosis Approach for Depression Using Vocal Source Features |
| title_full_unstemmed | Intelligent Diagnosis Approach for Depression Using Vocal Source Features |
| title_short | Intelligent Diagnosis Approach for Depression Using Vocal Source Features |
| title_sort | intelligent diagnosis approach for depression using vocal source features |
| topic | depression feature combination intelligent diagnosis random forest algorithm |
| url | https://hrcak.srce.hr/file/398891 |
| work_keys_str_mv | AT yuangao intelligentdiagnosisapproachfordepressionusingvocalsourcefeatures AT yinanxin intelligentdiagnosisapproachfordepressionusingvocalsourcefeatures AT lizhang intelligentdiagnosisapproachfordepressionusingvocalsourcefeatures |