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Detection of Chronic Kidney Disease by Using Artificial Neural Networks and Gravitational Search Algorithm

  • S. M. K. ChaitanyaEmail author
  • P. Rajesh Kumar
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 33)

Abstract

Chronic Kidney Disease (CKD) is a universal health issue attacking around 10% of the populace worldwide. This disease can be detected using Artificial Neural Networks approach along with optimizing technique called as Gravitational Search Algorithm. These networks are periodically used as strong classifiers during the diagnosis of a disease. The data has been collected from the UCI Machine Learning Repository, which is an MR image. From the collected data, 80% of it is used for training the neural networks and 20% is used for the testing purpose. In this paper, the algorithms like Artificial Neural Network with Gravitational Search Algorithm (ANN+GSA), Artificial Neural Network with Genetic Algorithm (ANN+GA), and K-nearest neighbor are used. The intent of this paper is to compare the performance of these two algorithms on the basis of accuracy, sensitivity, and specificity.

Keywords

ANN FFBP algorithm Feature extraction GSA UCI Machine Learning Repository 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of ECEG. V. P. College of Engineering (Autonomous)VisakhapatnamIndia
  2. 2.Department of ECEAndhra University College of Engineering (Autonomous)VisakhapatnamIndia

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