000			02764cam a2200325 a 4500
003			EG-GiCUC
008			210412s2021 ua db f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.20.04.M.Sc.2021.Ra.P
100	0		_aRaghda Essam Abdelrazek Ali
245	1	0	_aPrediction of diabetic obese patients using machine learning techniques / _cRaghda Essam Abdelrazek Ali ; Supervised Hatem Mohamed Elkadi , Soha Safwat Labib , Yasmin Saad Ibrahim
246	1	5	_aالتنبؤ بالاصابه بمرض السكرى للمصابين بالبدانه بإستخدام تقنيات التعلم الآلى
260			_aCairo : _bRaghda Essam Abdelrazek Ali , _c2021
300			_a112 Leaves : _bcharts , maps ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Information Systems
520			_acomputing. 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			_aIssued also as CD
653		4	_aDiabetes
653		4	_aMachine learning
653		4	_aObesity
700	0		_aHatem Mohamed Elkadi , _eSupervisor
700	0		_aSoha Safwat Labib , _eSupervisor
700	0		_aYasmin Saad Ibrahim , _eSupervisor
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c80624 _d80624