000			03132nam a2200325 a 4500
003			EG-GiCUC
008			220305s2022 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.18.07.M.Sc.2022.Ma.L
100	0		_aManal Makram Hana Abdelmalek
245	1	0	_aLearning approach for heart a machine diseases diagnosis / _cManal Makram Hana Abdelmalek ; Supervised Ammar Mohammed , Nesrine Ali Abdelzim
246	1	5	_aنهج تعلم الآلة لتشخيص أمراض القلب
260			_aCairo : _bManal Makram Hana Abdelmalek , _c2022
300			_a151 P. : _bcharts , facsimiles ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Infomation System Technogy
520			_aCardiovascular diseases have been the leading cause of death worldwide for several decades, in both industrialised and developing countries. Early detection of cardiac diseases and ongoing medical supervision can lower mortality rates, reduce unnecessary hospitalizations, manage resources, and save money. However, reliable detection of cardiac disease in all cases and 24-hour consultation with a physician are not possible due to the additional intelligence, time, and expertise required. In this thesis, heart disease prediction can be based on high-accuracy machine learning techniques. As a result, the suggested system's most essential feature was that as soon as any real-time parameter of the patient exceeded the threshold, the recommended doctor was immediately contacted via GSM technology. Nowadays, therefore, data growth in the biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. In this thesis, machine learning is used to classify IHD in patients with heart disease based on patient history, lab results, radiology results, medical reports, operations, patients{u2019} supplies, and pathological findings. A total of 15032 patients{u2019} data with a maximum of 74 features, including historic, symptomatic, and pathologic findings, were collected from ASUSH hospital. In this thesis, different levels of accuracy were achieved, depending on the machine learning algorithms used and the dataset (size and features) that was extracted. The collected features showed high correlations with IHD, which achieved high accuracy. The dataset was split randomly into training and testing sets. The results show that neural network, random forest, and SVM classifiers respectively give significantly better results than naïve bayes, decision trees, logistic regression, KNN, and K-Means classifiers
530			_aIssued also as CD
650		0	_aTechnology
653			_aGlobal System for Mobile communications
653			_aMachine diseases diagnosis
653			_aSupport Vector Machine
700	0		_aAmmar Mohammed , _eSupervisor
700	0		_aNesrine Ali Abdelzim , _eSupervisor
905			_aEnas _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c84410 _d84410