Mental Disorder Classification And Prediction

Abstract

While mental illness continues to cast a wide shadow over the world, its classification remains a complex dance. Thankfully, recent years have brought significant leaps forward in understanding and categorizing these conditions, fueled by powerful new diagnostic tools and computational approaches. This snapshot dives into the current state of mental health problem classification, highlighting exciting trends. Imagine machines learning from vast amounts of data, uncovering hidden patterns and boosting diagnostic accuracy like never before. These advancements hold the potential to revolutionize how we identify and address mental health challenges. This research introduces an innovative method for categorizing mental health disorders through machine learning techniques. The study utilizes a diverse dataset encompassing a range of mental health conditions, demographic details, and behavioral patterns. The primary objective is to develop a robust model capable of accurately categorizing individuals into different mental health groups based on their distinctive characteristics. The study addresses a classification challenge, aiming to differentiate individuals among Major Depressive Disorder (MDD), Obsessive-Compulsive Disorder (OCC), Anxiety, Post-Traumatic Stress Disorder (PTSD), sleeping problems, and loneliness. In this research, various ML Algorithms, including the Logistic Regression Algorithm and Random Forest Algorithm, are employed for the classification of mental health disorders. This endeavor aims to broaden the project's scope, showcasing additional capabilities while upholding accuracy.