Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/3190
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKowsalya R-
dc.contributor.authorSasikala G-
dc.contributor.authorSangeetha Priya J-
dc.date.accessioned2023-07-07T10:34:34Z-
dc.date.available2023-07-07T10:34:34Z-
dc.date.issued2010-09-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/3190-
dc.description.abstractUrinary System includes kidneys, bladder, ureters and urethra. This is the major system involves electrolyte balance of the body and filters the blood and excretes the waste products in the form urine. Even the small disturbance in the renal function will step in a disasters manifestation. Among them we are considering the two diseases that affect the system are acute cystitis and acute nephritis. This paper presents the implementation of three supervised learning algorithms, ZeroR, J48 and Naive Bayes in WEKA environment. The classification models were trained using the data collected from 120 patients. The trained models were then used for predicting the acute cystitis or acute nephritis of the patients. The prediction accuracy of the classifiers was evaluated using 10-fold cross validation and the results were compared.en_US
dc.language.isoen_USen_US
dc.publisherGlobal Journal of Computer Science and Technologyen_US
dc.subjectUrinary Systemen_US
dc.subjectUretersen_US
dc.subjectUrethraen_US
dc.subjectAcuteCystitisen_US
dc.subjectAcute Nephritisen_US
dc.subjectclassificationen_US
dc.subjectWEKAen_US
dc.titleACUTE CYSTITIS AND ACUTE NEPHRITIS PREDICTION USING MACHINE LEARNING TECHNIQUESen_US
dc.typeArticleen_US
Appears in Collections:National Journals

Files in This Item:
File Description SizeFormat 
ACUTE CYSTITIS AND ACUTE NEPHRITIS PREDICTION USING MACHINE LEARNING TECHNIQUES.docx11.88 kBMicrosoft Word XMLView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.