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Prediction of diabetic obese patients using machine learning techniques / (Record no. 80624)

MARC details
000 -LEADER
fixed length control field 02764cam a2200325 a 4500
003 - CONTROL NUMBER IDENTIFIER
control field EG-GiCUC
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210412s2021 ua db f m 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-GiCUC
Language of cataloging eng
Transcribing agency EG-GiCUC
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title eng
049 ## - LOCAL HOLDINGS (OCLC)
Holding library Deposite
097 ## - Thesis Degree
Thesis Level M.Sc
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number Cai01.20.04.M.Sc.2021.Ra.P
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Raghda Essam Abdelrazek Ali
245 10 - TITLE STATEMENT
Title Prediction of diabetic obese patients using machine learning techniques /
Statement of responsibility, etc. Raghda Essam Abdelrazek Ali ; Supervised Hatem Mohamed Elkadi , Soha Safwat Labib , Yasmin Saad Ibrahim
246 15 - VARYING FORM OF TITLE
Title proper/short title التنبؤ بالاصابه بمرض السكرى للمصابين بالبدانه بإستخدام تقنيات التعلم الآلى
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cairo :
Name of publisher, distributor, etc. Raghda Essam Abdelrazek Ali ,
Date of publication, distribution, etc. 2021
300 ## - PHYSICAL DESCRIPTION
Extent 112 Leaves :
Other physical details charts , maps ;
Dimensions 30cm
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Information Systems
520 ## - SUMMARY, ETC.
Summary, etc. computing. This contribution can help in managing and interpreting various types of medical data to support decision-making. Machine learning approaches have demonstrated that it is sufficient for such complicated tasks. Early prediction of diseases in healthcare sector is very important. Diabetes disease is one of the threatening diseases whose occurrence is growing alarmingly and expected to increase more and more by 2035. Obesity is considered to be a massive risk factor of type 2 diabetes, type 2 diabetes has been proposed as a leading cause of fatty liver disease progression, it also probably reflect the quick succession of obesity and resistant to insulin in type 2 diabetes. Machine learning techniques nowadays help in diseases prediction to avoid the probability of its occurrence as much as possible. In this thesis, we explore the use of the machine learning techniques in the design of medical classification predictive models derived from the patient{u2019}s data address the complexities of designing machine learning techniques for promoting clinical decision-taking. Four machine learning classifiers have been used in this study which are; K-Nearest Neighbor, Fuzzy K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network in order to detect non-alcoholic fatty liver disease and predict diabetes mellitus chronic disease.The used techniques are applied on a real dataset from Al-Kasr Al-Aini Hospital in Giza, Egypt
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Issued also as CD
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Diabetes
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Machine learning
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Obesity
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Hatem Mohamed Elkadi ,
Relator term
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Soha Safwat Labib ,
Relator term
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Yasmin Saad Ibrahim ,
Relator term
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Nazla
Reviser Revisor
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Shimaa
Reviser Cataloger
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
Holdings
Source of classification or shelving scheme Not for loan Home library Current library Date acquired Full call number Barcode Date last seen Koha item type Copy number
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة قاعة الرسائل الجامعية - الدور الاول 11.02.2024 Cai01.20.04.M.Sc.2021.Ra.P 01010110083232000 22.09.2023 Thesis  
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة مخـــزن الرســائل الجـــامعية - البدروم 11.02.2024 Cai01.20.04.M.Sc.2021.Ra.P 01020110083232000 22.09.2023 CD - Rom 83232.CD