Stress, anxiety, and depression are prevalent mental health conditions that significantly impact the quality of life for millions of individuals worldwide. Understanding the intricate dynamics of the human nervous system during these states is crucial for developing effective interventions and treatments. In this paper, we present a comprehensive study utilizing machine learning techniques to delve into the complexities of stress, anxiety, and depression and their effects on the human nervous system. By leveraging various physiological signals and behavioral data, we aim to uncover patterns, biomarkers, and predictive models that elucidate the underlying mechanisms of these conditions. Through interdisciplinary collaboration between neuroscience, psychology, and machine learning, our study provides valuable insights into the physiological manifestations of stress, anxiety, and depression, paving the way for personalized and data-driven approaches to mental health care.

Stress, anxiety, and depression are prevalent mental health conditions