Detection Of Depression And Anxiety Symptoms In University Students Using Machine Learning

Abstract

This report explores the application of machine learning techniques for the detection of depression and anxiety symptoms among university students. The prevalence of mental health issues in this demographic poses significant challenges to academic success and overall well-being. Traditional methods of identifying and addressing these concerns often fall short due to limitations such as stigma, subjective assessments, and resource constraints. Using diverse datasets encompassing demographic information, academic performance metrics, social media activity, and smartphone usage patterns, machine learning models are trained to recognize patterns indicative of mental health issues. The study evaluates various machine learning algorithms, including Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), k-Nearest Neighbors (KNN), and Naive Bayes (NB), to determine their effectiveness in predicting depression and anxiety symptoms. Ethical considerations such as data privacy, bias mitigation, and responsible model deployment are also addressed. The findings offer insights into the potential of machine learning to revolutionize mental health assessment in university settings, providing opportunities for early intervention and personalized support for students.