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dc.contributor.advisorSabuncuoğlu, İhsanen_US
dc.contributor.authorAral, Karca Duruen_US
dc.date.accessioned2016-01-08T18:14:24Z
dc.date.available2016-01-08T18:14:24Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/11693/15161
dc.descriptionAnkara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2009.en_US
dc.descriptionThesis (Master's) -- -Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references leaves 61-69en_US
dc.description.abstractFraud is the illegitimate act of violating regulations in order to gain personal profit. These kinds of violations are seen in many important areas including, healthcare, computer networks, credit card transactions and communications. Every year health care fraud causes considerable amount of losses to Social Security Agencies and Insurance Companies in many countries including Turkey and USA. This kind of crime is often seem victimless by the committers, nonetheless the fraudulent chain between pharmaceutical companies, health care providers, patients and pharmacies not only damage the health care system with the financial burden but also greatly hinders the health care system to provide legitimate patients with quality health care. One of the biggest issues related with health care fraud is the prescription fraud. This thesis aims to identify a data mining methodology in order to detect fraudulent prescriptions in a large prescription database, which is a task traditionally conducted by human experts. For this purpose, we have developed a customized data-mining model for the prescription fraud detection. We employ data mining methodologies for assigning a risk score to prescriptions regarding Prescribed Medicament- Diagnosis consistency, Prescribed Medicaments’ consistency within a prescription, Prescribed Medicament- Age and Sex consistency and Diagnosis- Cost consistency. Our proposed model has been tested on real world data. The results we obtained from our experimentations reveal that the proposed model works considerably well for the prescription fraud detection problem with a 77.4% true positive rate. We conclude that incorporating such a system in Social Security Agencies would radically decrease human-expert auditing costs and efficiency.en_US
dc.description.statementofresponsibilityAral, Karca Duruen_US
dc.format.extentxii, 75 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFraud Detectionen_US
dc.subjectPrescription Frauden_US
dc.subjectData Miningen_US
dc.subject.lccQA76.9.D343 A73 2009en_US
dc.subject.lcshData mining.en_US
dc.subject.lcshMedication abuse.en_US
dc.subject.lcshDrugs--Prescribing--Carrupt practices.en_US
dc.titlePrescription Fraud detection via data mining : a methodology proposalen_US
dc.typeThesisen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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