Abstract—The occurrence of Chronic Renal Disease (CRD), is also referred to as Chronic Kidney Disease (CKD). It depicts a medical condition that harms the kidneys and has an impact on a person’s overall health. End-stage renal disease and the patient’s eventual mortality can result from improper disease diagnosis and treatment. In the field of medical science, Machine Learning (ML) techniques have become a valuable tool and play a significant role in disease prediction. The development and validation of a predictive model for the prognosis of chronic renal disease is the aim of the proposed study. A dataset on chronic kidney disease with 400 samples was taken from the UCI Machine Learning Repository. Three machine learning classifiers—Logistic Regression (LR), Decision Tree (DT), and Support Vector Machine (SVM)—were used for analysis, and the bagging ensemble method was used to enhance the model’s performance. The machine learning classifiers were trained using the clusters of the dataset for chronic renal disease. The Kidney Disease Collection is then compiled using non-linear features and categories. The decision tree produces the best results, with an accuracy of 95%. Finally, we achieve the greatest accuracy of 97% by using the bagging ensemble approach.
 
Keywords—chronic renal disease, classification algorithms, random forest classifier, machine learning


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