000			05134namaa22004331i 4500
003			EG-GICUC
005			20260106105528.0
008			251215s2025 ua a|||frm||| 000 0 eng d
040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _bara
049			_aDeposit
082	0	4	_a005.1
092			_a005.1 _221
097			_aM.Sc
099			_aCai01.18.07.M.Sc.2024.Na.P
100	0		_aNader Said Mohammed Mohammed Morh, _epreparation.
245	1	0	_aPrevent fraud cases at health care systems / _cby Nader Said Mohammed Mohammed Morh ; Supervised Prof. Dr. Ahmed Mohamed Gadallah, Dr. Ahmed Hamza Mohamed.
246	1	5	_aمنع حالات الاحتيال في أنظمة الرعاية الصحية
264		0	_c2024.
300			_a143 Leaves : _billustrations ; _c30 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (M.Sc)-Cairo University, 2024.
504			_aBibliography: pages 138-143.
520		3	_aHealthcare fraud is a persistent and expanding issue in modern healthcare systems, resulting in substantial financial losses and negatively impacting the equitable distribution of medical resources. Documented practices include false billing, cost inflation, and claims for services not rendered. The transition to digital health records and claim systems has improved efficiency but also created new pathways for complex fraud that elude traditional auditing methods. To address this challenge, a scalable and accurate fraud detection framework based on Machine Learning (ML) techniques was proposed. The study involved the evaluation of nineteen supervised classification models, including linear classifiers, neural networks, and ensemble methods such as XGBoost and Extra Trees. Structured claim data was used as input, with suspicious patterns targeted for identification. Given the high-class imbalance typically found in fraud detection datasets, several resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), Random Over Sampling (ROS) and Random Under-Sampling (RUS) were applied. Additionally, statistical features were engineered to extract behavioral insights from financial variables, resulting in enriched datasets. A total of ten experimental configurations were implemented, with the best-performing models achieving up to 99.99% accuracy when combining feature engineering and resampling. The experiments were conducted using a publicly available dataset titled Healthcare Provider Fraud Detection Analysis, developed in 2022 by the University of California and published on Kaggle. As a practical reference, the Egyptian Company for Metro (ECM) was cited to demonstrate the system’s potential applicability in large institutional healthcare environments. The study concludes that ML offers a powerful tool for detecting healthcare fraud, especially when combined with effective preprocessing and domain- aware feature construction. The proposed framework provides a solid foundation for developing future real-time fraud detection systems that can enhance decision-making and reduce operational losses in healthcare delivery.
520		3	_aتتناول 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			_aIssues also as CD.
546			_aText in English and abstract in Arabic & English.
650		0	_aSoftware Engineering
650		0	_aهندسة البرمجيات
653		1	_aFraud Detection _aHealthcare _aMachine Learning _aPreprocessing _aFeature Engineering _aStatistical Feature Engineering _aاكتشاف الاحتيال _aالرعاية الصحية
700	0		_aAhmed Mohamed Gadallah _ethesis advisor.
700	0		_aAhmed Hamza Mohamed _ethesis advisor.
900			_b01-01-2024 _cAhmed Mohamed Gadallah _cAhmed Hamza Mohamed _UCairo University _FFaculty of Graduate Studies for Statistical Research _DDepartment of Software Engineering
905			_aShimaa _eEman Ghareb
942			_2ddc _cTH _e21 _n0
999			_c176732