A Novel Classification Method Based on Multilayer Perceptron-Artificial Neural Network Technique for Diagnosis of Chronic Kidney Disease

Abstract

Background: Chronic kidney disease is one of the most common diseases. The early diagnosis of this disease will reduce the length of treatment and decrease high medical costs. In recent years, the use of computer techniques in data mining and intelligent algorithms has accelerated the early diagnosis of this disease. One of the intelligent methods to diagnose this disease is artificial intelligence networks. Objectives: This study aimed to investigate the diagnosis of chronic kidney disease using an artificial intelligence network based on the multilayer perceptron method. Methods: The data of laboratory samples were collected from 140 healthy people and patients with chronic kidney disease. After preprocessing and normalization, the data were given to a multilayer perceptron and the accuracy of disease diagnosis was evaluated. All analyses were performed using MATLAB software. Results: The simulation showed a 98% accuracy of diagnosis using the proposed model. Conclusions: The results of real data suggested that the proposed system was more effective and faster than other methods in the diagnosis of acute kidney disease and it can be used as a physician assistant tool in clinical practice. In addition, it can be a cost-effective method for patients.