Chronic Kidney Disease Diagnosis System using Sequential Backward Feature Selection and Artificial Neural Network

Abstract

The number of factors that can be categorized into the diagnosis of Chronic Kidney Disease (CKD) at an early stage makes information about the diagnosis of the disease divided into information that has many influences and has little influence. This study aims to select diagnoses in medical records with the most influential information on chronic kidney disease. The first step is to select a diagnosis with much influence by implementing the Sequential Backward Feature Selection (SBFS). This algorithm eliminates features that are considered to have little influence when compared to other features. In the second step, the features of the best diagnoses are used as input to the Artificial Neural Network (ANN) classification algorithm. The results obtained from this study are information in the form of the best diagnoses that have much influence on chronic kidney disease and the accuracy results based on the selected diagnoses. Based on the study results, 15 features are considered the best of the 18 features used to achieve 88% accuracy results. Compared with conventional methods, this method still requires consideration from the medical staff because it is not a final diagnosis for patients.