Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification.
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| Title: | Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification. |
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| Authors: | Islam, S. M. Rakibul1 smrakibulislam34@gmail.com, Yunus, Shaykh1 shaykhyunus2000@gmail.com, Shakil, Rashiduzzaman2 rashiduzzaman.diucse@gmail.com, Johora, Fatema Tuz1,3 meem8494@gmail.com, Rajbongshi, Aditya1 aditya0001@uftb.ac.bd, Sutradhar, Sujon Chandra4 sujon0001@uftb.ac.bd |
| Source: | Telkomnika. Jun2026, Vol. 24 Issue 3, p915-925. 11p. |
| Subjects: | Machine learning, Shapley Additive Explanations, College students, Artificial intelligence, Mental health, Classification of mental disorders, Early diagnosis |
| Abstract: | Depression is a widespread mental health condition characterized by enduring feelings of persistent sadness, loss of interest, and impaired daily functioning. Untreated depression can result in significant implications, such as academic failure, social isolation, and even suicide. This study presents a machine learning (ML)-based framework for classifying depression severity among university students using the Zahir depression scale dataset, comprising 478 responses categorized into mild, moderate, severe, and profound depression. In order to address the issue of class imbalance, we utilized the synthetic minority over sampling technique (SMOTE) on the dataset. In addition, seven different ML algorithms are employed to classify the severity of depression, and each algorithm's efficiency is determined by four performance evaluation metrics. Among the applied ML classifiers, extra tree classifier outperformed with an average accuracy of 97.85% and 95.75% precision, 95.76% recall, and 95.75% F1-score. To enhance interpretability, the shapley additive explanations (SHAP) method was integrated to identify influential features, providing transparency and insight into the model's decision process. The proposed framework demonstrates that combining explainable artificial intelligence (XAI) with traditional ML can support healthcare professionals in early depression screening and datadriven mental health interventions. [ABSTRACT FROM AUTHOR] |
| Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195172778 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Islam%2C+S%2E+M%2E+Rakibul%22">Islam, S. M. Rakibul</searchLink><relatesTo>1</relatesTo><i> smrakibulislam34@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yunus%2C+Shaykh%22">Yunus, Shaykh</searchLink><relatesTo>1</relatesTo><i> shaykhyunus2000@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Shakil%2C+Rashiduzzaman%22">Shakil, Rashiduzzaman</searchLink><relatesTo>2</relatesTo><i> rashiduzzaman.diucse@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Johora%2C+Fatema+Tuz%22">Johora, Fatema Tuz</searchLink><relatesTo>1,3</relatesTo><i> meem8494@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Rajbongshi%2C+Aditya%22">Rajbongshi, Aditya</searchLink><relatesTo>1</relatesTo><i> aditya0001@uftb.ac.bd</i><br /><searchLink fieldCode="AR" term="%22Sutradhar%2C+Sujon+Chandra%22">Sutradhar, Sujon Chandra</searchLink><relatesTo>4</relatesTo><i> sujon0001@uftb.ac.bd</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Telkomnika%22">Telkomnika</searchLink>. Jun2026, Vol. 24 Issue 3, p915-925. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22College+students%22">College students</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+health%22">Mental health</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+of+mental+disorders%22">Classification of mental disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Early+diagnosis%22">Early diagnosis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Depression is a widespread mental health condition characterized by enduring feelings of persistent sadness, loss of interest, and impaired daily functioning. Untreated depression can result in significant implications, such as academic failure, social isolation, and even suicide. This study presents a machine learning (ML)-based framework for classifying depression severity among university students using the Zahir depression scale dataset, comprising 478 responses categorized into mild, moderate, severe, and profound depression. In order to address the issue of class imbalance, we utilized the synthetic minority over sampling technique (SMOTE) on the dataset. In addition, seven different ML algorithms are employed to classify the severity of depression, and each algorithm's efficiency is determined by four performance evaluation metrics. Among the applied ML classifiers, extra tree classifier outperformed with an average accuracy of 97.85% and 95.75% precision, 95.76% recall, and 95.75% F1-score. To enhance interpretability, the shapley additive explanations (SHAP) method was integrated to identify influential features, providing transparency and insight into the model's decision process. The proposed framework demonstrates that combining explainable artificial intelligence (XAI) with traditional ML can support healthcare professionals in early depression screening and datadriven mental health interventions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.12928/TELKOMNIKA.v24i3.27651 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 915 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Shapley Additive Explanations Type: general – SubjectFull: College students Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Mental health Type: general – SubjectFull: Classification of mental disorders Type: general – SubjectFull: Early diagnosis Type: general Titles: – TitleFull: Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Islam, S. M. Rakibul – PersonEntity: Name: NameFull: Yunus, Shaykh – PersonEntity: Name: NameFull: Shakil, Rashiduzzaman – PersonEntity: Name: NameFull: Johora, Fatema Tuz – PersonEntity: Name: NameFull: Rajbongshi, Aditya – PersonEntity: Name: NameFull: Sutradhar, Sujon Chandra IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16936930 Numbering: – Type: volume Value: 24 – Type: issue Value: 3 Titles: – TitleFull: Telkomnika Type: main |
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