Machine Learning and Deep Learning Multi-Modal Approaches in Mental Health Diagnosis: A Comprehensive Review

This paper receives recent advancements in Machine learning (ML) and Deep Learning (DL) approaches applied to mental health diagnosis over the past years. We examine a range of techniques, including Supervised and Unsupervised learning, Natural Language Processing (NLP), and image recognition in neuroimaging. The review encompasses various data sources, such as electronic health records, social media and wearable sensor outputs, targeting diagnoses of depression, anxiety, bipolar disorder, schizophrenia, PTSD, anorexia nervosa and ADHD. The analysis explores the use of various machine learning (ML) models such as support vector machines, decision trees, random forests and ensemble methods. Findings also includes deep learning models such as CNNs, RNNs, transformer-based models, have been applied to multi-modal data such as texts, speech and image data to extract meaningful features and patterns that are indicative of mental health conditions. This review concludes by discussing implications of integrating ML and DL approaches into clinical practice, emphasizing the importance of interdisciplinary collaboration among data scientists and mental health professionals.