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 compe