Open AccessPrediction with Prevention of the Chronic Kidney Disease by Implementing the Convolutional Neural Network Algorithm
Author(s) -
Nilam Kadale,
Pranav Hugar,
Kiran Panchal,
Kirtiraj Botre
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1323
Subject(s) - kidney disease , convolutional neural network , random forest , computer science , classifier (uml) , artificial neural network , algorithm , disease , population , artificial intelligence , machine learning , stage (stratigraphy) , medicine , intensive care medicine , paleontology , environmental health , biology
Around 12% of population suffers from kidney diseases whose symptoms are unknown to them until last stage. CKD is diagnosed if evidence of kidney damage has been present for more than 3 months. Approximate 75% patients are undiagnosed because of no early prediction. Improved prediction model with accurate rate to identify early stage can help to predict disease in early stages. In this paper by using two algorithms i.e., Convolutional neural network and and random forest classifier a predicted model is build whose output will be, if the given user has CKD or not.