Fuzzy Expert System for Diagnosing Diabetes Mellitus (Record no. 65913)

MARC details
000 -LEADER
fixed length control field 01474nam a22001337a 4500
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Sabagi, Mubashreen
-- 16MSIT25
-- Supervisor - Dr. Akbar Hussain Jalbani
245 ## - TITLE STATEMENT
Title Fuzzy Expert System for Diagnosing Diabetes Mellitus
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Nawabshah:
Name of publisher QUEST,
Year of publication 2019.
300 ## - PHYSICAL DESCRIPTION
Number of Pages 33p.
500 ## - GENERAL NOTE
General note ABSTRACT<br/><br/><br/>Diabetes is a dangerous disease in which the human body cannot produce proper quantity of insulin. Diabetes will also develop heart disease, kidney disease, blindness, nerve damage, and blood vessel damage. This research uses Mamdani type expert systems for a diabetes diagnosis. Fuzzy expert system is a group of membership functions and rules. Fuzzy based expert systems are leaning toward numerical computing.<br/>In which diagnose the diabetes using four different type of input age, gender and two blood test HbA1c and FPG using MATLAB Fuzzy Logic Designer in which first create fuzzy sets through fuzzification and fuzzy inference engine create the rules and facts and apply the rules on those facts to take the appropriate decision on the basis of given information that person is affected by diabetes or not and provides the description of result. Finally defuzzification method is used to convert the fuzzy output set into a crisp output. The output of the proposed fuzzy inference system is encouraging and provides the accuracy of 81.2 %.<br/>
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Department of Information Technology
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://tinyurl.com/yc4aybyb
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis and Dissertation
Holdings
Withdrawn status Lost status Home library Current library Date acquired Accession Number Koha item type
    Research Section Research Section 18/10/2019 MP/55-696 Thesis and Dissertation
    Research Section Research Section 21/12/2023 MP/47-552 Thesis and Dissertation