MARC details
| 000 -LEADER |
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| fixed length control field |
03132nam a2200325 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
EG-GiCUC |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220305s2022 ua dh f m 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-GiCUC |
| Language of cataloging |
eng |
| Transcribing agency |
EG-GiCUC |
| 041 0# - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| 049 ## - LOCAL HOLDINGS (OCLC) |
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| Holding library |
Deposite |
| 097 ## - Thesis Degree |
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| Thesis Level |
M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
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| Classification number |
Cai01.18.07.M.Sc.2022.Ma.L |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Manal Makram Hana Abdelmalek |
| 245 10 - TITLE STATEMENT |
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| Title |
Learning approach for heart a machine diseases diagnosis / |
| Statement of responsibility, etc. |
Manal Makram Hana Abdelmalek ; Supervised Ammar Mohammed , Nesrine Ali Abdelzim |
| 246 15 - VARYING FORM OF TITLE |
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| Title proper/short title |
نهج تعلم الآلة لتشخيص أمراض القلب |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Cairo : |
| Name of publisher, distributor, etc. |
Manal Makram Hana Abdelmalek , |
| Date of publication, distribution, etc. |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
151 P. : |
| Other physical details |
charts , facsimiles ; |
| Dimensions |
30cm |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Infomation System Technogy |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Cardiovascular 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 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
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| Additional physical form available note |
Issued also as CD |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Technology |
| 653 ## - INDEX TERM--UNCONTROLLED |
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| Uncontrolled term |
Global System for Mobile communications |
| 653 ## - INDEX TERM--UNCONTROLLED |
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| Uncontrolled term |
Machine diseases diagnosis |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Support Vector Machine |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Ammar Mohammed , |
| Relator term |
|
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Nesrine Ali Abdelzim , |
| Relator term |
|
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| Cataloger |
Enas |
| 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 |
