Type-2 Diabetes (Bangladeshi Patients)

Published: 17 July 2024| Version 2 | DOI: 10.17632/vxnyysk9vc.2
Sharia Arfin Tanim,


This dataset comprises data collected from the Pabna Diabetes Hospital, Pabna, Bangladesh, as part of a study aimed at developing predictive models for accurately classifying diabetes patients using machine learning (ML) and deep learning (DL) techniques. The dataset is notable for its diverse demographic representation, making it valuable for training robust ML models. Data Collection: - Source: Pabna Diabetes Hospital, Pabna, Bangladesh. - Method: Retrospective collection. - Anonymization: All data underwent rigorous anonymization to ensure confidentiality and privacy. - Informed Consent: Obtained from all participants following the ethical guidelines of the Pabna Diabetes Hospital (Ref No.: CERT/PADAS/PAB-64). Dataset Composition: - Total Participants: 465 female patients aged 21 years or older. - Diabetic Patients: 373 - Non-Diabetic Patients: 92 - Patients with Serum Insulin Data: 131 - Patients without Serum Insulin Data: 334 - Patients with Genetic Predisposition: 293 - Patients without Genetic Predisposition: 172 Variables: - Number of pregnancies - Age - BMI (Body Mass Index) - Systolic and Diastolic Blood Pressures - Genetic Information - Serum Insulin Levels - Triceps Skinfold Thickness - Plasma Glucose Concentration (after 2 hours in an oral glucose tolerance test) - Diabetes Status (Diabetic or Non-Diabetic) Research Implications: The dataset supports the development and validation of ML and DL models for diabetes classification, leveraging local healthcare data to enhance model generalization across diverse populations. Publication Reference: M.S. Reza et al., "Heliyon," 10 (2024): e24536.



Machine Learning, Medical Care, Database