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

Area of Science:

• Public Health
• Psychology
• Data Science

Background:

• Quarantine measures significantly impacted population mental health globally.
• Limited data exists on the specific mental health dynamics within Bangladesh during quarantine periods.
• Understanding these dynamics is crucial for targeted public health interventions.

Purpose of the Study:

• To introduce and describe the RHMCD-20 dataset, a comprehensive resource on mental health in Bangladesh during quarantine.
• To highlight the dataset's utility for predictive modeling, societal impact analysis, and intergenerational mental health studies.
• To facilitate the development of machine learning applications in healthcare.

Main Methods:

• A structured survey was administered across diverse demographic groups in Bangladesh.
• Data collection encompassed variables such as age, gender, occupation, and stress levels.
• The dataset was curated to support correlation analysis and predictive modeling.

Main Results:

• The RHMCD-20 dataset provides rich, multi-variable insights into quarantine-related mental health.
• It enables the identification of factors influencing stress and mental well-being.
• The data supports understanding the societal and workplace impacts of quarantine on mental health.

Conclusions:

• The RHMCD-20 dataset is a valuable resource for researchers and policymakers in Bangladesh.
• It offers nuanced insights for evidence-based mental health interventions and policy development.
• The dataset supports advancements in precision mental healthcare and dynamic forecasting models.