A Comprehensive Review of Machine Learning Algorithms for Depression Prediction with a Fuzzy Logic Perspective

Abstract

Depression is a pervasive mental-health disorder characterized by persistent low mood, anhedonia, cognitive impairment, and somatic symptoms that impair daily functioning. In this systematic review we examined 162 peer-reviewed studies published between 2010 and 2024, identified via a PRISMA-guided search of PubMed, IEEE Xplore, Scopus, Web of Science and Google Scholar using keywords related to “depression”, “prediction”, “machine learning”, “neuro-fuzzy” and “fuzzy logic”; studies were included if they reported empirical predictive models on human subjects, supplied quantitative performance metrics, and were written in English, and were screened independently by two reviewers with data extraction covering algorithms, modalities, evaluation metrics, interpretability and ethical considerations. We synthesize advances across EHR analyses, NLP of clinical notes and social media, multimodal audio–visual signals, and physiological monitoring, and identify persistent gaps, most notably a shortage of transparent, generalizable models and a near-absence of systematic evaluations of fuzzy or hybrid neuro-fuzzy approaches in the depression domain. Unlike previous reviews that focus narrowly on performance or a single modality, this paper is the first to quantify and compare interpretability, generalizability and ethical risk across traditional ML and fuzzy/hybrid models, highlight where fuzzy inference can encode clinical expertise to manage symptom uncertainty, and propose a practical, hybrid neuro-fuzzy framework and evaluation checklist aimed at producing accurate, explainable and ethically responsible depression screening tools deployable in real-world clinical settings.