This study advances mental-health diagnostics by integrating supervised and unsupervised machine-learning methods with a strong ethical lens. We review and contextualize the current literature on ML applications for depression detection, noting the heavy reliance of supervised models on labelled data and the comparative under-exploration of unsupervised approaches. Using the public Depression Dataset-which comprises actigraph recordings from depressed patients and healthy controls and includes demographic and clinical attributes such as timestamps, activity counts, gender, age and Montgomery-Åsberg Depression Rating Scale HYPERLINK "http://kaggle.com" \h score we preprocess and engineer features capturing circadian rhythms and variability in motor activity. We then apply multiple clustering algorithms (K-means, hierarchical clustering and DBSCAN) to identify latent subgroups of depression severity, evaluating cluster validity via the silhouette score, Davies-Bouldin index and Calinski-Harabasz index. A supervised support-vector machine classifier trained on labelled severity categories serves as a baseline, and we find that unsupervised clustering achieves competitive performance while revealing nuanced patterns not captured by labelled categories. We visualise cluster structures and compare performance metrics to illustrate the benefits and limitations of each method. Finally, we analyse cluster composition relative to gender and socio-economic variables to highlight potential biases in the data and underscore the need for fairness-aware models and interpretability. By combining supervised and unsupervised techniques and explicitly addressing ethical considerations, this work contributes to more accurate, transparent and equitable ML-based depression diagnosis.
| Published in | Machine Learning Research (Volume 10, Issue 2) |
| DOI | 10.11648/j.mlr.20251002.17 |
| Page(s) | 158-164 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Machine Learning, Unsupervised Learning, Depression Diagnosis, Clustering, Ethical AI
| [1] |
APA Style
Akanbi, A. A. (2025). Innovative Machine Learning Approaches for Accurate and Ethical Depression Diagnosis: Insights and Recommendations. Machine Learning Research, 10(2), 158-164. https://doi.org/10.11648/j.mlr.20251002.17
ACS Style
Akanbi, A. A. Innovative Machine Learning Approaches for Accurate and Ethical Depression Diagnosis: Insights and Recommendations. Mach. Learn. Res. 2025, 10(2), 158-164. doi: 10.11648/j.mlr.20251002.17
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.mlr.20251002.17,
author = {Abiodun Abdulghani Akanbi},
title = {Innovative Machine Learning Approaches for Accurate and Ethical Depression Diagnosis: Insights and Recommendations},
journal = {Machine Learning Research},
volume = {10},
number = {2},
pages = {158-164},
doi = {10.11648/j.mlr.20251002.17},
url = {https://doi.org/10.11648/j.mlr.20251002.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20251002.17},
abstract = {This study advances mental-health diagnostics by integrating supervised and unsupervised machine-learning methods with a strong ethical lens. We review and contextualize the current literature on ML applications for depression detection, noting the heavy reliance of supervised models on labelled data and the comparative under-exploration of unsupervised approaches. Using the public Depression Dataset-which comprises actigraph recordings from depressed patients and healthy controls and includes demographic and clinical attributes such as timestamps, activity counts, gender, age and Montgomery-Åsberg Depression Rating Scale HYPERLINK "http://kaggle.com" \h score we preprocess and engineer features capturing circadian rhythms and variability in motor activity. We then apply multiple clustering algorithms (K-means, hierarchical clustering and DBSCAN) to identify latent subgroups of depression severity, evaluating cluster validity via the silhouette score, Davies-Bouldin index and Calinski-Harabasz index. A supervised support-vector machine classifier trained on labelled severity categories serves as a baseline, and we find that unsupervised clustering achieves competitive performance while revealing nuanced patterns not captured by labelled categories. We visualise cluster structures and compare performance metrics to illustrate the benefits and limitations of each method. Finally, we analyse cluster composition relative to gender and socio-economic variables to highlight potential biases in the data and underscore the need for fairness-aware models and interpretability. By combining supervised and unsupervised techniques and explicitly addressing ethical considerations, this work contributes to more accurate, transparent and equitable ML-based depression diagnosis.},
year = {2025}
}
TY - JOUR T1 - Innovative Machine Learning Approaches for Accurate and Ethical Depression Diagnosis: Insights and Recommendations AU - Abiodun Abdulghani Akanbi Y1 - 2025/12/19 PY - 2025 N1 - https://doi.org/10.11648/j.mlr.20251002.17 DO - 10.11648/j.mlr.20251002.17 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 158 EP - 164 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20251002.17 AB - This study advances mental-health diagnostics by integrating supervised and unsupervised machine-learning methods with a strong ethical lens. We review and contextualize the current literature on ML applications for depression detection, noting the heavy reliance of supervised models on labelled data and the comparative under-exploration of unsupervised approaches. Using the public Depression Dataset-which comprises actigraph recordings from depressed patients and healthy controls and includes demographic and clinical attributes such as timestamps, activity counts, gender, age and Montgomery-Åsberg Depression Rating Scale HYPERLINK "http://kaggle.com" \h score we preprocess and engineer features capturing circadian rhythms and variability in motor activity. We then apply multiple clustering algorithms (K-means, hierarchical clustering and DBSCAN) to identify latent subgroups of depression severity, evaluating cluster validity via the silhouette score, Davies-Bouldin index and Calinski-Harabasz index. A supervised support-vector machine classifier trained on labelled severity categories serves as a baseline, and we find that unsupervised clustering achieves competitive performance while revealing nuanced patterns not captured by labelled categories. We visualise cluster structures and compare performance metrics to illustrate the benefits and limitations of each method. Finally, we analyse cluster composition relative to gender and socio-economic variables to highlight potential biases in the data and underscore the need for fairness-aware models and interpretability. By combining supervised and unsupervised techniques and explicitly addressing ethical considerations, this work contributes to more accurate, transparent and equitable ML-based depression diagnosis. VL - 10 IS - 2 ER -
Department of Computer Science, Nile University of Nigeria, Abuja, Nigeria
