A Cross-Sectional Dataset of Sleep Disorder Indicators Among Bangladeshi University Students
Description
Data were collected (using a cross-sectional study design) from students of five public universities in Bangladesh: Hajee Mohammad Danesh Science & Technology University (HSTU), University of Dhaka (DU), University of Rajshahi (RU), University of Chittagong (CU), and Khulna University of Engineering & Technology (KUET). The data collection process involved structured questionnaires administered through both Google Forms and in-person interviews. This approach enabled the capture of a comprehensive snapshot of students’ demographic, lifestyle, physiological, and academic characteristics associated with sleep patterns. The dataset consists of 619 responses and 12 features, including both categorical and numerical variables. It is designed for data analysis and machine learning tasks, particularly for sleep disorder classification, with a focus on identifying conditions such as Insomnia and Sleep Apnea among university students. This dataset can be used for classification, exploratory data analysis, feature engineering, and predictive modeling research. Key Features of the Dataset: Department: Academic department of the student. Gender: Gender of the student (Male/Female). Age: Age of the student in years. Sleep Duration: Average number of hours the student sleeps per day, represented in categorized ranges. Quality of Sleep: Self-reported evaluation of sleep quality, ranging from Excellent to Very Poor. Physical Activity Level: Daily level of physical activity performed by the student. Stress Level: Self-reported stress level, ranging from Very Low to Very High. BMI Category: Body Mass Index classification of the student (Underweight, Normal, Overweight, Obese). Blood Pressure: Measured systolic and diastolic blood pressure values of the student. Heart Rate (bpm): Resting heart rate measured in beats per minute. Daily Steps: Approximate number of steps taken per day, represented in categorized ranges. Academic Level: Current academic level of the student (Level-1 to Level-4). Target Class (Sleep Disorder): Indicates the presence or type of sleep disorder (No Sleep Disorder, Insomnia, Sleep Apnea). Target Class Details: No Sleep Disorder: The student does not exhibit any symptoms of sleep disorders. Insomnia: The student experiences difficulty falling asleep or staying asleep, resulting in poor sleep quality. Sleep Apnea: The student experiences interruptions in breathing during sleep, leading to disrupted sleep patterns and potential health risks. Acknowledgement* The data collection process was conducted solely by the author for academic and research purposes. All responses were gathered voluntarily, and the dataset has been prepared to support research, educational use, and machine learning experimentation in the domain of sleep health and disorder analysis. The study protocol was reviewed and approved by the Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinaj-5200, Bangladesh.
Files
Steps to reproduce
Najma Akter Lopa, Sultan Mahamud Opu, Sourav Roy, Mst Ifat Zahan Soma and Pankaj Bhowmik*, "AI-Driven Analysis of Sleep Disorders in Bangladeshi University Students Using Clinical and Behavioral Insights", 5th IEEE International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE 2026). DOI: 10.1109/ICECTE69292.2026.11429299 Live app for SLEEP DISORDER PREDICTION: https://dss-sleepdisorder.streamlit.app/
Institutions
- Hajee Mohammad Danesh Science and Technology UniversityRangpur Division, Dinajpur