DiaBD: A Diabetes Dataset for Enhanced Risk Analysis and Research in Bangladesh

This dataset contains 5,288 patient records, covering 14 independent attributes related to demographics, clinical parameters, and medical history. Key features include age, gender, pulse rate, blood pressure (systolic and diastolic), glucose level, BMI, and family history of diabetes, hypertension, and cardiovascular disease. Each patient entry is labeled with a binary diabetes status (diabetic or non-diabetic), making it suitable for predictive modeling and risk assessment.
Designed to facilitate machine learning applications, this dataset supports the development of diabetes detection models, risk stratification, and personalized management strategies. The comprehensive feature set enables researchers to explore patterns in diabetes prevalence and related health factors, thereby contributing to improved early diagnosis and targeted interventions.