Machine Learning in Mental Healthcare: Improving Diagnosis and Treatment of Anxiety and Depression

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Abstract

Mental health disorders, particularly anxiety and depression, constitute a global public health crisis affecting over 700 million individuals worldwide. Traditional diagnostic methodologies rely heavily on subjective self-reporting and clinician observation, leading to significant delays in accurate diagnosis and treatment initiation. This paper provides a comprehensive review of the application of machine learning (ML) techniques in improving the diagnosis and treatment outcomes for anxiety and depression. We examine supervised learning models including Support Vector Machines (SVM), Random Forests, and deep neural networks applied to clinical datasets, electronic health records, and neuroimaging data. Additionally, we explore Natural Language Processing (NLP) approaches for sentiment analysis in social media and therapy transcripts, and the integration of wearable biosensor data with ML pipelines. Our analysis demonstrates that ML models consistently achieve diagnostic accuracy rates of 75–92% across varied datasets, outperforming traditional screening instruments. We also discuss current ethical challenges, data privacy concerns, explainability of AI models, and propose a framework for responsible clinical integration. The findings suggest that ML-driven tools, when deployed ethically, hold substantial promise for augmenting clinical decision-making, personalizing treatment pathways, and extending mental healthcare access to underserved populations.