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It was developed to address the limited sample size of commonly used renal research benchmarks and provides more than twice the number of instances compared to the standard UCI Chronic Kidney Disease dataset.\nThe dataset includes 26 variables, combining demographic information (Age), physical examination data (Blood Pressure), and 24 laboratory biomarkers, such as Serum Creatinine (Sc), Blood Urea (Bu), Hemoglobin (Hemo), and Specific Gravity (Sg). Each record is annotated with a binary target variable (Class) that categorizes patients as either Healthy (n = 481) or Kidney Disease (n = 507), enabling reliable development and evaluation of diagnostic machine learning models.\n-----------------------------------------------------------------------------------------------------------------\nNote: This study involves retrospective clinical data obtained from diagnostic laboratory records. The dataset was collected with institutional authorization for academic research purposes. All patient records were fully anonymized prior to dataset preparation, and no personally identifiable information was included in the dataset. The research procedures followed the ethical principles outlined in the Declaration of Helsinki for research involving human subjects. Informed consent for clinical testing was obtained from patients by the diagnostic center as part of routine medical procedures. The dataset used in this study contains only anonymized laboratory and clinical measurements and therefore does not allow identification of individual participants."},"citation:producer":{"citation:producerName":"Dr. Muhammad Towfiqur Rahman","citation:producerAffiliation":"University of Asia Pacific"},"citation:datasetContact":{"citation:datasetContactName":"Islam, Md. Masudul","citation:datasetContactAffiliation":"Bangladesh University of Business and Technology","citation:datasetContactEmail":"masudulislam11@gmail.com"},"author":{"citation:authorName":"Islam, Md. 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