Email this article Machine Learning for Predicting Mental Health Disorders: A Data-Driven Approach to Early Intervention
Abstract
Mental health disorders, including depression, anxiety, and schizophrenia, can be better managed with early detection and intervention. This paper investigates the role of machine learning (ML) in predicting mental health disorders based on various factors such as patient history, social media data, and behavioral patterns. Using supervised learning models like random forests and support vector machines (SVMs), we explore how these algorithms can identify early warning signs of mental health issues. The paper also highlights the challenges of working with sensitive mental health data and the ethical implications of using AI for mental health diagnosis.
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