Please use this identifier to cite or link to this item: http://103.99.128.19:8080/xmlui/handle/123456789/115
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dc.contributor.authorMrinmoy Dey, Mrinmoy Dey-
dc.date.accessioned2018-10-01T12:07:43Z-
dc.date.available2018-10-01T12:07:43Z-
dc.date.issued2017-01-06-
dc.identifier.urihttp://103.99.128.10:8080/xmlui/handle/123456789/115-
dc.description.abstractAbstract—Diabetes is the most regular disease in medical science. It can affect the organs of the human body. Due to the handling both uncertain diabetic medical and clinical data to diabetes diagnosis system is a complex problem However, a computer-base diagnosis system for diabetes would help to enhance the accuracy of the diagnosis and reduce the time and cost. This paper describes an effective new approach “Adaptive Neuro-Belief Rule Based System (ANBRBS)” with Evidential Reasoning (ER) to diagnose diabetes, which can reduce the errors and medical uncertainties. This paper used the medical and clinical real data to implement and test of this proposed system. It has been observed that, this new adaptive methodology provides more reliable diabetes diagnosis result in percentage and recommendationsen_US
dc.language.isoenen_US
dc.publisherCUETen_US
dc.subjectDiabetes, Neural Network; Belief Rule Base; ER Algorithm, Neuro-BRB, Diagnosisen_US
dc.titleICECE 2016en_US
dc.title.alternativeAdaptive Neuro-Belief Rule Based Diabetes Diagnosis Systemen_US
Appears in Collections:proceedings in ETE

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