ICECE 2016

dc.contributor.author	Mrinmoy Dey, Mrinmoy Dey
dc.date.accessioned	2018-10-01T12:07:43Z
dc.date.available	2018-10-01T12:07:43Z
dc.date.issued	2017-01-06
dc.identifier.uri	http://103.99.128.10:8080/xmlui/handle/123456789/115
dc.description.abstract	Abstract—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 recommendations	en_US
dc.language.iso	en	en_US
dc.publisher	CUET	en_US
dc.subject	Diabetes, Neural Network; Belief Rule Base; ER Algorithm, Neuro-BRB, Diagnosis	en_US
dc.title	ICECE 2016	en_US
dc.title.alternative	Adaptive Neuro-Belief Rule Based Diabetes Diagnosis System	en_US