Open AccessInnovative and Smart Methodology towards Kidney Disease Detection in Earlier Stage
Author(s) -
S P Karthi,
S Guganesh,
K. Kavitha,
Juhi Kumar,
Dinesh Selvakumar
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/994/1/012025
Subject(s) - premise , computer science , artificial neural network , matlab , field (mathematics) , kidney disease , disease , task (project management) , artificial intelligence , intensive care medicine , machine learning , medicine , pathology , engineering , systems engineering , philosophy , linguistics , mathematics , pure mathematics , operating system
In modern era of medical field, kidney diseases are the foremost common diseases found in the majority of the populations in subsequent years. The essential is diagnosing the problem and however advanced task that ought to be accomplished precisely with proficiently. In most of the cases, kidney condition leads to loss of life. Diagnosing could be a tough thus the intention of the research is to develop a neural-trained system to spot kidney condition risk of the patients. The Artificial Neural network will collect the data from the previous method and it will be helpful to identify the chance factors on the premise of provided data. This paper uses neural techniques for diagnosing of the kidney disease. MATLAB simulation was employed as a development tool.