Mind Matters: USDI-30 Depression Dataset of Bangladeshi University Students for Machine Learning Analysis

Published: 10 October 2024| Version 1 | DOI: 10.17632/57yvy3gx8f.1
Contributors:
,
,
,
,

Description

The Mind Matters: USDI-30 Depression Dataset of Bangladeshi University Students for Machine Learning Analysis contains data from 823 university students, collected from two prominent institutions in Bangladesh: Daffodil International University (DIU) and the University of Dhaka. The dataset is based on the University Student Depression Inventory (USDI-30) scale, which measures 30 key psychological and emotional indicators of depression, such as mood, motivation, self-worth, and social engagement. Each participant's data was collected with individual informed consent, ensuring that all respondents were aware of the purpose of the study, the confidentiality of their data, and their right to withdraw at any time. Demographic information was also collected with consent, including age, gender, academic year, and field of study, providing richer context for analysis. To further enhance the dataset’s validity, all responses were reviewed and validated by domain experts, including licensed psychologists, ensuring that the data accurately reflects the mental health status of the respondents. Key features: 1. 823 validated responses from DIU and University of Dhaka students. 2. 30 depression indicators based on the USDI-30 scale, covering comprehensive mental health dimensions. 3. Demographic data including age, gender, academic year, and field of study, collected with informed consent. 4. Domain expert validation by psychologists, ensuring high data quality and relevance. 5. The dataset is cleaned and pre-processed, making it ready for machine learning and predictive analysis. Depression Labelling Steps: The answers of USDI-30 questions converted into numerical score such as Never = 0, Rarely (less than one day) = 1, Occasionally (1-2 days) = 2, Frequently (3-4 days) = 3, and Most of the time (5-7 days) = 4. The depression levels based on the USDI Scale: 1. 0-30 = No Depression 2. 30-60 = Mild Depression 3. 60-90 = Moderate Depression 4. 90-120 = Severe Depression This dataset serves as a valuable resource for mental health researchers, data scientists, and AI practitioners. It provides a solid foundation for developing predictive models, understanding depression trends, and facilitating early intervention strategies for university students at risk of depression.

Files

Institutions

Daffodil International University, Chittagong University of Engineering and Technology

Categories

Artificial Intelligence, Depression, Mental Health, Machine Learning, Big Data, Deep Learning

Licence