Kidney disease is a major global health concern that requires timely diagnosis and effective monitoring to prevent severe complications and improve patient outcomes. This data article presents BD-KDD, a structured clinical dataset designed to facilitate research on kidney disease diagnosis. The dataset was collected retrospectively from electronic medical records obtained from Popular Diagnostic Center, Savar Branch, Dhaka, Bangladesh, following institutional authorization for academic research. The BD-KDD dataset contains 988 patient records with 26 variables, including demographic attributes, physiological measurements, biochemical laboratory tests, urinalysis indicators, hematological parameters, comorbidity indicators, and clinical symptoms. Key laboratory features include serum creatinine, blood urea, blood glucose, sodium, potassium, hemoglobin, packed cell volume, red blood cell count, and white blood cell count, along with urinalysis indicators such as specific gravity, albumin, and sugar levels. Each record is assigned a binary diagnostic label representing either healthy individuals or kidney disease cases based on clinical evaluation and laboratory findings. The curated dataset includes 481 healthy and 507 kidney disease cases and is provided in CSV format together with a dataset dictionary describing variable definitions and coding schemes. BD-KDD offers a valuable resource for biomedical data analysis, health informatics research, and the development of machine learning–based diagnostic models and clinical decision support systems for renal health assessment.
Rahman et al. (Sun,) studied this question.