Open AccessDetecting Chronic Kidney Disease from Blood Samples using Neural Networks
Author(s) -
Mohamed Ghaisan Latheef,
Rajasvaran Logeswaran,
Nurul Haniza Mohtar
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1712/1/012008
Subject(s) - artificial neural network , kidney disease , computer science , artificial intelligence , disease , machine learning , chronic disease , data mining , medicine , intensive care medicine , pathology
This paper proposes an artificial neural network approach to automatically detecting Chronic Kidney Disease through fluid samples taken from patients. The rationale for developing such a system is given, as well as possible benefits to the patients and medical industry. Similar systems proposed in the industry and for diagnosing chronic kidney disease through other approaches such as classification algorithms are explored. A dataset to train the neural network on is collected and features analysed, as well as methodology and tools to be used in the development of the neural network.