Prevent fraud cases at health care systems / (Record no. 176732)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05134namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GICUC |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20260106105528.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251215s2025 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.1 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 005.1 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.18.07.M.Sc.2024.Na.P |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Nader Said Mohammed Mohammed Morh, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Prevent fraud cases at health care systems / |
| Statement of responsibility, etc. | by Nader Said Mohammed Mohammed Morh ; Supervised Prof. Dr. Ahmed Mohamed Gadallah, Dr. Ahmed Hamza Mohamed. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | منع حالات الاحتيال في أنظمة الرعاية الصحية |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 143 Leaves : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc)-Cairo University, 2024. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 138-143. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | Healthcare fraud is a persistent and expanding issue in modern healthcare <br/>systems, resulting in substantial financial losses and negatively impacting the <br/>equitable distribution of medical resources. Documented practices include false <br/>billing, cost inflation, and claims for services not rendered. The transition to <br/>digital health records and claim systems has improved efficiency but also created <br/>new pathways for complex fraud that elude traditional auditing methods. <br/>To address this challenge, a scalable and accurate fraud detection framework <br/>based on Machine Learning (ML) techniques was proposed. The study involved <br/>the evaluation of nineteen supervised classification models, including linear <br/>classifiers, neural networks, and ensemble methods such as XGBoost and Extra <br/>Trees. Structured claim data was used as input, with suspicious patterns targeted <br/>for identification. <br/>Given the high-class imbalance typically found in fraud detection datasets, <br/>several resampling techniques such as Synthetic Minority Over-sampling <br/>Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), Random Over <br/>Sampling (ROS) and Random Under-Sampling (RUS) were applied. <br/>Additionally, statistical features were engineered to extract behavioral insights <br/>from financial variables, resulting in enriched datasets. A total of ten <br/>experimental configurations were implemented, with the best-performing <br/>models achieving up to 99.99% accuracy when combining feature engineering <br/>and resampling. <br/>The experiments were conducted using a publicly available dataset titled <br/>Healthcare Provider Fraud Detection Analysis, developed in 2022 by the <br/>University of California and published on Kaggle. As a practical reference, the <br/>Egyptian Company for Metro (ECM) was cited to demonstrate the system’s <br/>potential applicability in large institutional healthcare environments. <br/> The study concludes that ML offers a powerful tool for detecting healthcare <br/>fraud, especially when combined with effective preprocessing and domain-<br/>aware feature construction. The proposed framework provides a solid foundation <br/>for developing future real-time fraud detection systems that can enhance <br/>decision-making and reduce operational losses in healthcare delivery. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | تتناول هذه الدراسة تطوير إطار اكتشاف الاحتيال في الرعاية الصحية باستخدام تقنيات التعلم الآلي (Machine Learning) مع التركيز على المعالجة المسبقة هندسة الميزات. تم اختبار 19 نموذجًا تصنيفيًا مثل XGBoost وExtra Trees باستخدام بيانات المطالبات، مع تطبيق تقنيات SMOTE وADASYN وROS وRUS لموازنة البيانات. حققت النماذج أداءً عاليًا بدقة وصلت إلى 99.99% عند دمج تحسين الميزات وإعادة التوزيع. تقدم الدراسة إطارًا عمليًا لمنع الاحتيال في مؤسسات ذات بيانات مطالبات كبيرة ومعقدة، مع استخدام الشركة المصرية لإدارة وتشغيل المترو (ECM) كنموذج تطبيقي لدراسة إمكانية تطبيق الإطار في بيئات مؤسسية ذات طابع تشغيلي |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issues also as CD. |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Software Engineering |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | هندسة البرمجيات |
| 653 #1 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Fraud Detection |
| -- | Healthcare |
| -- | Machine Learning |
| -- | Preprocessing |
| -- | Feature Engineering |
| -- | Statistical Feature Engineering |
| -- | اكتشاف الاحتيال |
| -- | الرعاية الصحية |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ahmed Mohamed Gadallah |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ahmed Hamza Mohamed |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2024 |
| Supervisory body | Ahmed Mohamed Gadallah |
| -- | Ahmed Hamza Mohamed |
| Universities | Cairo University |
| Faculties | Faculty of Graduate Studies for Statistical Research |
| Department | Department of Software Engineering |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Shimaa |
| Reviser Names | Eman Ghareb |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 15.12.2025 | 92799 | Cai01.18.07.M.Sc.2024.Na.P | 01010110092799000 | 15.12.2025 | 15.12.2025 | Thesis |