RHMCD-20 dataset: Identify rapid human mental health depression during quarantine life using machine learning

Abstract

The RHMCD-20 dataset offers a thorough investigation of the dynamics of mental health in Bangladesh while under quarantine. The structured survey that was distributed to different demographic groups yielded a dataset that included a wide range of variables, such as age, gender, occupation, and stress levels. Predictive modelling, understanding the effects of quarantine on the workplace and society, and intergenerational insights are all greatly enhanced by this dataset. The dataset allows intelligent algorithms to be developed by bridging the gap between machine learning and healthcare. Although sampling bias is one of the limitations of correlation analysis, it does improve understanding. This presents opportunities for improving precision in mental health management, fostering interdisciplinary collaborations, and creating dynamic forecasting models. Researchers and policymakers can benefit greatly from the RHMCD-20 dataset, which offers nuanced insights into mental health experiences during quarantine and informs evidence-based interventions and policies. groundwork for innovative methodologies, steering the trajectory of informed decision-making in dynamic energy landscapes.

Keywords

• Mental health
• Machine learning
• COVID-19 quarantine
• Isolation effects