Advancements in Machine Learning-Based Mental Health Prediction: A Comprehensive Review

Mental health is as important as physical health because it consists of our mental, societal, and emotional welfare. It affects people’s feelings, opinions, and behavior. It impacts our ability to handle stress, communicate with others, and make informed decisions. Mental health is vital at each phase of life, from childhood to maturity. There exists an unbreakable bond between mental and physical well-being, with mental health being shaped by numerous interconnected factors such as emotional, social, cultural, spiritual, and physical dimensions. Mental healthiness illnesses pose significant challenges globally, impacting countless people and putting an immense stress on healthcare systems. One of the most common diseases is depression. Depression is a common mental health condition that not only leaves you feeling depressed all the time but also affects your thoughts, food, sleeping, and behavior patterns. Timely intervention and outcomes depend on early detection and effective prognosis of these illnesses. The paper commences with an overview of the prevalence and impact of depression, underscoring the need for effective prediction models. This paper provides a complete overview of research on mental health prediction using ML algorithms. The review begins by discussing the occurrence plus impact of psychological health conditions, emphasizing necessity for innovative prediction models. It explores the application of ML algorithms, counting SVM, ANN, DT, RF, and ensemble methods, in predicting various mental health conditions. Different sources are examined, highlighting the strengths and limitations associated with each and also discuss challenges and limitations in current research.