Journal Article2025Cited by 17

DiaBD: A diabetes dataset for enhanced risk analysis and research in Bangladesh

Tabia Tanzin Prama, Md. Jobayer Rahman, Marzia Zaman, Farhana Sarker, Khondaker A. Mamun

Data in Brief

Elsevier BV, Vol. 61, pp. 111746, ISBN: 2352-3409

  • Good Health and Well-being — Relevance: 94%
DOI: 10.1016/j.dib.2025.111746Open AccessURL

Abstract

Diabetes is a chronic condition affecting millions worldwide and severely impacts health and quality of life. According to the International Diabetes Federation (IDF), over 463 million adults, which is 9.3% of the global population, live with diabetes. Diabetes ranks among the most prevalent chronic diseases and was the ninth-leading cause of mortality in 2019, with 4.2 million deaths reported. This article introduces DiaBD, a novel dataset of 5,288 patient records from Bangladesh, designed to address critical gaps in diabetes research and aid in healthcare planning, risk analysis, and predictive modelling. The dataset comprises 14 attributes including age, gender, clinical vitals (pulse rate, systolic and diastolic blood pressure, glucose levels), anthropometrics (height, weight, body mass index (BMI)), family history of diabetes and hypertension, cardiovascular disease (CVD), and stroke, with a dependent attribute, Diabetic, indicates whether an individual has diabetes or not. The dataset ensures demographic diversity and precise measurements, supporting the study of diabetes and its related health issues. Features like CVD and stroke enable broader research on comorbidities. This dataset facilitates machine learning applications, risk assessment, and personalized healthcare strategies. Researchers can explore the links between diabetes, hypertension, CVD, and stroke, while healthcare providers and policymakers can leverage DiaBD to identify trends, allocate resources efficiently, and enhance public health strategies.

Keywords

  • stroke
  • Diabetes mellitus
  • cardiovascular disease
  • comorbidities
  • family history
  • body mass index
  • diabetes risk prediction
  • Bangladesh
  • blood glucose
  • anthropometry

CCDS Authors

Farhana Sarker, PhD
Farhana Sarker, PhD

Farhana Sarker, PhD

  • Director, WiSE Wing

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ICDMW, IEEE Access

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Cited By

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  1. 1.Tabia Tanzin Prama, Md. Jobayer Rahman, Marzia Zaman, Farhana Sarker, Khondaker A. Mamun. (2025). DiaBD: A diabetes dataset for enhanced risk analysis and research in Bangladesh. Data in Brief, 61, 111746[10.1016/j.dib.2025.111746]
  2. 2.Shrinit Babel, Shrinit Babel, Sunit Babel, Sunit Babel, John Hodgson, Enrico Camporesi. (2025). Few-Shot Transfer Learning for Diabetes Risk Prediction Across Global Populations. Medicina, 62(1), 7[10.3390/medicina62010007]
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  5. 5.Cheng Ma, Zhongyang Li, Jiangli Lin, Bin Wu, Shuailian Zhao, Xiaojing Yao, Xiuling Zhu, Xiangyun Wei, Shishan Zhou. (2025). Revealing the inhibition mechanism of ultrasound-assisted purified dried apricot polyphenols on α-amylase: Insights from multispectral and computational analyses. Food Bioscience, 74, 108014[10.1016/j.fbio.2025.108014]
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  14. 14.Arifa Ahmed, Barna Biswas, Mohammad Shahidullah, Durga Shahi, Fariha Akhter, Mst Shurovi Akter, Muslima Begom Riipa, Md Shayakh Alam. (2025). Optimizing Diabetes Prediction Using Ensemble Learning Techniques for Enhanced Accuracy. , 227–232[10.1109/amict65811.2025.11402798]
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