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.
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References
Vital TP, Nagesh MYV, Anuradha T, Samanthula AR (2016) A neural network approach for classification of kidney disease dataset collected from Visakhapatnam of A.P., India. Int J Innovative Res Sci Eng 2(2)
Panwong P, lam-On N (2016) Predicting transitional interval of kidney disease stages 3 to 5 using data mining method. In: 2016 second Asian conference on defence technology (ACDT)
Pourghahestani FA, Rashedi E (2015) Object detection in images using artificial neural network and improved binary gravitational search algorithm. In: 2015 4th Iranian joint congress on fuzzy and intelligent systems
Chakraborty D, Saha S, Maity S (2015) Training feedforward neural networks using hybrid flower pollination-gravitational search algorithm. In: 2015 1st international conference on futuristic trend in computational analysis and knowledge management (ABLAZE-2015)
Kumar K, Abhishek B Artificial neural networks for diagnosis of kidney stones disease. Published Online July 2012 in MECS https://doi.org/10.5815/ijitcs
Ramya S, Radha N Diagnosis of chronic kidney disease using machine learning algorithms. https://doi.org/10.15680/ijircce.2016.0401049
Vijayarani S, Dhayanand S (2015) Data mining classification algorithms for kidney disease prediction. Int J Cybern Inform (IJCI) 4(4)
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Chaitanya, S.M.K., Rajesh Kumar, P. (2019). Detection of Chronic Kidney Disease by Using Artificial Neural Networks and Gravitational Search Algorithm. In: Saini, H., Singh, R., Patel, V., Santhi, K., Ranganayakulu, S. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 33. Springer, Singapore. https://doi.org/10.1007/978-981-10-8204-7_44
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DOI: https://doi.org/10.1007/978-981-10-8204-7_44
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