Innovative Machine Learning Approaches for Accurate and Ethical Depression Diagnosis: Insights and Recommendations

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.