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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Sujon Chandra Sutradhar
University of Frontier Technology
Computer Science & IT

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Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification S. M. Rakibul Islam; Shaykh Yunus; Rashiduzzaman Shakil; Fatema Tuz Johora; Aditya Rajbongshi; Sujon Chandra Sutradhar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27651

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 data driven mental health interventions.
Comparative evaluation of classical and machine learning methods for medical image enhancement Md. Mehedi Hasan; Sujon Chandra Sutradhar; Zannatul Ferdushie; Rabeya Basri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27700

Abstract

Medical imaging is critical for diagnostic accuracy, yet raw images often suffer from noise and low contrast. This study provides a comparative evaluation of classical methods, namely the Laplace transform (LT), Sobel operator (SO), and histogram equalization (HE), against a data-driven convolutional neural network (CNN) using the musculoskeletal radiographs (MURA) and Human Metapneumovirus (HMPV) lung computed tomography (CT) datasets. While quantitative analysis shows that HE and SO significantly outperform other methods in isolated contrast enhancement and edge definition, they often introduce artifacts. In contrast, the CNN based approach demonstrates superior detail preservation and entropy, offering a more balanced and adaptive solution for diverse diagnostic requirements. Our findings statistically validate that although classical operators remain highly effective for specific boundary detection tasks, machine learning (ML) frameworks provide the most robust performance for cross-modality image enhancement, bridging the gap between raw data acquisition and clinical interpretation.
Co-Authors Aditya Rajbongshi Fatema Tuz Johora Md. Mehedi Hasan Rabeya Basri Rashiduzzaman Shakil S. M. Rakibul Islam Shaykh Yunus Zannatul Ferdushie