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Prediction of Kidney Failure Using Artificial Neural

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dc.contributor.author Abd Alla, Sara Mohamed Ahmed Mohamed
dc.contributor.author Supervisor - Eltahir Mohamed Hussein
dc.date.accessioned 2015-01-05T12:05:40Z
dc.date.available 2015-01-05T12:05:40Z
dc.date.issued 2014-05-11
dc.identifier.citation Abd Alla,Sara Mohamed Ahmed Mohamed.Prediction of Kidney Failure Using Artificial Neural/Sara Mohamed Ahmed Mohamed Abd Alla;Eltahir Mohamed Hussein.-khartoum:Sudan University of Science and Technology,College of Engineering,2014.-60p:ill;28cm.-M.Sc. en_US
dc.identifier.uri http://repository.sustech.edu/handle/123456789/9527
dc.description Thesis en_US
dc.description.abstract This research intents to assess the application of artificial neural network in predicting the kidney failure disease. Kidney failure disease is being observed as a serious challenge to the field of medical with its impact on a mass population of the world. This work explored and analyzed the data generated from 60 kidney patients in many hospitals and hemodialysis centers using data mining technique . This is done by using Artificial Neural Network technique to select the weight, and connectivity structure to determine system for input variables learning. This work provides Physicians with an instrument assess the dialysis service performance The study for prediction of kidney failure has been carried out using Feed forward back propagation and Cascade forward back propagation algorithms. The results of this study demonstrate that an ANN model with variables consisting of (urea, creatinine, potassium, sodium, calcium, phosphorus and uric acid) is classified a total of 60 patients correctly to normal and abnormal. The best network model produced prediction accuracy of 98.3 percent is given by Feed Forward Back Propagation network (FFBP) . en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science and Technology en_US
dc.subject Renal failure en_US
dc.subject Neural Networks industrial en_US
dc.subject kidney patients en_US
dc.subject urea en_US
dc.subject potassium
dc.subject creatinine
dc.subject Civil Engineering
dc.title Prediction of Kidney Failure Using Artificial Neural en_US
dc.title.alternative تشخيص الفشل الكلوي عن طريق العصبية الاصطناعية en_US
dc.type Thesis en_US


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