Signature-Based Methods for Data Streams
Data Mining and Knowledge Discovery
Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts
IEEE Expert: Intelligent Systems and Their Applications
Application of Genetic Algorithm and k-Nearest Neighbour Method in Medical Fraud Detection
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Mining the Knowledge Mine: The Hot Spots Methodology for Mining Large Real World Databases
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Evolutionary Hot Spots Data Mining - An Architecture for Exploring for Interesting Discoveries
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Using ethnography to design a mass detection tool (MDT) for the early discovery of insurance fraud
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Neural Data Mining for Credit Card Fraud Detection
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Synthesizing Test Data for Fraud Detection Systems
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Detecting outlier samples in multivariate time series dataset
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
ISMS '10 Proceedings of the 2010 International Conference on Intelligent Systems, Modelling and Simulation
A survey of multiple classifier systems as hybrid systems
Information Fusion
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Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs.