Machine Learning for Mental Health: A Systematic Study of Seven Approaches for Detecting Mental Disorders

Mental disorders are a prevalent issue among teenagers. The widespread use of smartphones and social media has revolutionized the way individuals communicate and exchange information with millions of people using these technologies every day. As a result, vast amounts of data are generated, which can be harnessed to improve mental health detection. The increasing prevalence of mental health issues and the demand for quality healthcare services have led to research exploring the potential of machine learning (ML) to address these challenges. This paper provides a systematic study of seven ML approaches used in previous studies to detect mental disorders. The study examines the datasets employed, the accuracy achieved, and the limitations of each ML approach. The seven ML approaches studied in this paper are Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO), Long Short-Term Memory (LSTM), Random Forest (RF), Logistic Regression (LR), Artificial Neural Networks (ANN), and eXtreme Gradient Boosting (XGBoost). These approaches have been utilized in various studies to detect mental disorders and this paper aims to provide a comprehensive understanding of their effectiveness. The findings indicate that machine learning approaches have demonstrated significant potential for the detection of mental disorders, with promising implications for enhancing healthcare services. Additionally, the paper discusses the open research challenges and future directions for mental health.