Assessing the Mental Health Impact of COVID-19 on the US Population: A Large-Scale Survey Analysis Using SMOTE

The coronavirus (COVID-19) outbreak, recognized as one of the deadliest health crises in recent history, swiftly affected over 200 countries. The pandemic has posed complex challenges, specifically affected not only the rising number of cases but also deeply influenced individual mental health. This research explores the significant mental health implications of the COVID-19 pandemic on the US population, analyzed through a large-scale survey of 25,136 participants across various ages, economic backgrounds, and chronic disease status. We categorized mental health status into three risk levels: low, moderate, and high, based on the key features influencing stress levels. Notably, our findings reveal that the age group 25-54 years exhibited higher anxiety levels compared to other age groups. The implementation of Extreme Gradient Boosting (XGBoost) with Synthetic Minority Over-sampling (SMOTE) Technique for balancing data yielded impressive accuracy rates: 94.55% for high risk, 90.73% for moderate risk, and 77.77% for low risk, respectively. These results significantly outperformed the Random Forest (RF) model in both imbalanced and SMOTE balanced datasets. Furthermore, the study identified high obesity and chronic diseases, such as bronchitis, as factors exacerbating stress levels. This research contributes valuable insights to the mental health condition prediction during COVID-19 pandemic by underlining the importance of targeted interventions for high-risk groups.