Advances in Data Science and Adaptive Analysis

Recent research indicates a rising occurrence of mental health issues among children and adolescents globally during the COVID-19 pandemic. However, the existing literature reveals a notable inconsistency in the reported prevalence rates. College students, in the midst of a transitional phase, confront numerous stressors associated with young adulthood, alongside the academic demands specific to their stage in life. A common challenge faced by college students is the difficulty in recognizing mental health problems and a reluctance to seek help. Both schools and colleges have not been spared from the psychological challenges brought on by the unexpected crisis of the COVID-19 pandemic. In light of these circumstances, this study endeavors to identify and elucidate the characteristics of depression, anxiety, and stress resulting from the COVID-19 pandemic, given its profound repercussions on adolescents. It is also worth exploring whether adolescents with a history of early life stress (ELS) are particularly vulnerable to these mental health challenges. This research aims to shed light on how the pandemic has affected college students, potentially leading to the development of anxiety, stress, and depression during and in the aftermath of the pandemic. To achieve these objectives, a research design involving both descriptive and diagnostic elements has been employed to investigate the prevalence of depression, anxiety, and stress among college students. Primary data have been collected using a straightforward random sampling technique. The research employs various tools, including demographic information about respondents, examining the COVID-19 pandemic’s contextual factors, exploring coping behaviors during the pandemic, and self-reported assessments of depression, anxiety, and stress. Furthermore, to gain insights into the impact of coping behaviors, appropriate statistical methods have been employed to examine how adolescents have been affected by depression, anxiety, and stress following the COVID-19 pandemic. Leveraging machine learning classifiers, a predictive dashboard has been created to estimate the severity of depression, anxiety, and stress experienced by college students based on significant factors associated with their symptoms.