Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia

This paper describes our experience with applying data mining techniques to the problem of fraud detection in spatio-temporal health data in Medicare Australia. A modular framework that brings together disparate data mining techniques is adopted. Several generally applicable techniques for extracting features from spatial and temporal data are also discussed. The system was evaluated with input from domain experts and was found to achieve high hit rates. We also discuss some lessons drawn from the experience. Keywords-fraud detection; spatio-temporal data; health data; propositionalisation; local outlier factor; sequence prediction.

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Kee Siong Ng, Yin Shan, D. Wayne Murray, Alison Sutinen, B. Schwarz, David Jeacocke, J. Farrugia
• Keywords: Data Mining, Health Data, ICDM 2010, Local Outlier Factor, Spatio-temporal Health Data