Math and Music: A Deep Analysis on Music Habits and Mental Health Disorders

This study explores the relationship between music-listening habits and mental health disorders such as anxiety, depression, insomnia, and OCD using a data-driven approach. Analyzing a diverse, self-reported dataset, machine learning models—including linear regression, Random Forests, and neural networks—were applied to predict mental health outcomes. While advanced models captured some nonlinear patterns, overall predictive power remained modest, indicating that music habits alone cannot fully explain mental health variations. Challenges such as sample bias, limited variables, and the self-reported nature of data were noted. The findings highlight the multifactorial nature of mental health and suggest that future research should incorporate broader lifestyle and environmental factors alongside refined modeling techniques to better understand these complex interactions.