Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification.

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Bibliographic Details
Title:	Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification.
Authors:	Islam, S. M. Rakibul¹ smrakibulislam34@gmail.com, Yunus, Shaykh¹ shaykhyunus2000@gmail.com, Shakil, Rashiduzzaman² rashiduzzaman.diucse@gmail.com, Johora, Fatema Tuz^1,3 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]
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Database:	Engineering Source

Description
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]
ISSN:	16936930
DOI:	10.12928/TELKOMNIKA.v24i3.27651