This study focuses on the development and validation of a urine-based diagnostic model for early detection of Chronic Kidney Disease (CKD). Only urine indicators, hypertension, diabetes mellitus, coronary artery disease, appetite, pedal edema, and anemia, are selected based on their relevance to CKD. A decision tree algorithm is utilized for model development, with specific parameters set for optimal performance. The model is trained and evaluated using two datasets, demonstrating promising results in terms of 100% true positive and true negative rates in validation study. The findings highlight the potential clinical significance and applicability of the developed model for timely interventions in CKD patients.
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