Synthetic Depression Risk Factors

This synthetic Depression Risk Factors Dataset is designed for educational and research purposes in the fields of data science, healthcare analytics, and mental health research. It contains various demographic, lifestyle, and health-related factors that may influence the likelihood of depression. The dataset can be used to develop predictive models, assess mental health risks, and explore the relationship between personal characteristics, behaviour patterns, and depression.

Dataset Features:

• **Age: **The age of the individual.
• Marital Status: The individual's marital status.
• Education Level: The highest level of education completed by the individual.
• Number of Children: The number of children the individual has.
• Smoking Status: Whether the individual is a smoker.
• Physical Activity Level: The individual's level of physical activity.
• Employment Status: Whether the individual is employed.
• Income: The annual income of the individual.
• Alcohol Consumption: The level of alcohol consumption.
• Dietary Habits: The individual's eating habits.
• Sleep Patterns: The quality of the individual's sleep.
• History of Mental Illness: Whether the individual has a history of mental illness.
• History of Substance Abuse: Whether the individual has a history of substance abuse.
• Family History of Depression: Whether there is a family history of depression.
• Chronic Medical Conditions: Whether the individual has chronic medical conditions.

Distribution

Usage

This dataset is ideal for a variety of mental health-related applications:

• Depression Prediction: Build machine learning models to predict the likelihood of depression based on personal and lifestyle factors.
• Risk Factor Analysis: Identify factors contributing to depression, and assess the impact of variables such as lifestyle, history, and income.
• Predictive Modeling: Develop models to predict mental health outcomes by analyzing various demographic and health indicators.
• Healthcare Research: Explore the relationship between various social, psychological, and physical factors and the likelihood of depression.
• Public Health Analysis: Understand patterns in mental health and guide public health initiatives focused on mental illness prevention and management.

Coverage

This synthetic dataset is anonymized, ensuring privacy and compliance with data protection regulations. It is designed for research and learning purposes, offering a diverse set of individuals and conditions for analysis and model building.

License

CC0 (Public Domain)

Who Can Use It

• Data Science Practitioners: Ideal for practising data preprocessing, classification tasks, and model development.
• Mental Health Professionals and Researchers: Useful for studying the impact of lifestyle, demographics, and personal history on depression.
• Public Health Analysts: For identifying trends in mental health and formulating strategies for mental illness prevention.
• Healthcare Analysts: To develop models for predicting mental health outcomes and designing interventions.