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 |