Burn Depression Checklist Dataset

Dataset Overview:

The "Burn Depression Checklist Dataset" consists of 2,600 rows and 25 features, each capturing a distinct aspect of depressive symptoms. This dataset is intended to be a resource for researchers and practitioners in the field of mental health, particularly those interested in the study and treatment of depression. The dataset's features were selected based on well-established criteria in psychological assessment, and they collectively provide a comprehensive view of an individual's emotional and psychological state.

Features Description:

The 25 features in this dataset include a variety of depressive symptoms such as:

• Feeling sad or down in the dumps: Captures the general feeling of sadness or depression.
• Feeling unhappy or blue: Measures the extent of unhappiness or feeling "blue."
• Crying spells or tearfulness: Records the frequency of crying or tearfulness.
• Feeling discouraged, hopeless, or worthless: These features assess feelings of discouragement, hopelessness, and low self-esteem, which are key indicators of depression.
• Guilt or shame: This feature captures the extent to which an individual feels guilty or ashamed.
• Criticizing yourself or others: Records self-critical or other-critical thoughts.
• Difficulty making decisions: Assesses the level of difficulty an individual has in making decisions, often a symptom of depression.
• Loss of interest in family, friends, or colleagues: Measures the decline in interest in social interactions.
• Loss of motivation or interest in activities: Captures the reduction in motivation or interest in work, hobbies, or other activities.
• Avoiding work or other activities: Tracks the avoidance of work or other routine activities.
• Feeling tired or difficulty sleeping: These features record feelings of fatigue and sleeping issues, which are common in depression.
• Decreased or increased appetite: Assesses changes in appetite, another common symptom of depression.
• Suicidal thoughts or plans: Includes critical features related to suicidal ideation, a severe symptom of depression.

Usage and Application:

Researchers and data scientists can utilize this dataset for a wide range of applications, including but not limited to:

• Statistical Analysis: Perform exploratory data analysis (EDA) to understand the distribution and correlation of depressive symptoms.
• Machine Learning: Train predictive models to identify individuals at risk of severe depression or suicidal ideation.
• Clinical Research: Explore the dataset for clinical insights that could lead to improved diagnostic tools or therapeutic approaches.
• Public Health Studies: Use the data to study the prevalence and impact of depressive symptoms in different populations.

Instructions for Access and Use:

The dataset is freely available for download from IEEE Dataport. Users are encouraged to cite this dataset in any publications or presentations that make use of the data. The citation format is provided on the dataset's IEEE Dataport page. Before using the dataset, users should review the terms of use and ensure that any analysis or publication complies with ethical standards for research, particularly when dealing with sensitive mental health data.

Researchers should also consider anonymizing the data further if used in a way that could potentially lead to the identification of individuals. The dataset is intended for research and educational purposes, and not for diagnostic or therapeutic use in a clinical setting without proper validation.