Chronic Kidney Disease is one of the most common metabolic diseases. The challenge in this area is a pre-processing problem. Artificial Intelligence techniques have been implemented over medical disease diagnoses successfully. Classification systems aim clinicians to pr More
Chronic Kidney Disease is one of the most common metabolic diseases. The challenge in this area is a pre-processing problem. Artificial Intelligence techniques have been implemented over medical disease diagnoses successfully. Classification systems aim clinicians to predict the risk factors that cause Chronic Kidney Disease. To address this challenge, we introduce an effective model to investigate the role of pre-processing and machine learning techniques for classification problems in the diagnosis of Chronic Kidney Disease. The model has four stages including, Pre-processing, Feature Selection, Classification, and Performance. Missing values and outliers are two problems that are addressed in the pre-processing stage. Many classifiers are used for classification. Two tools are conducted to reveal model performance for the diagnosis of Chronic Kidney Disease. The results confirmed the superiority of the proposed model over its counterparts.
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