Researchers at Bahria University Have Published New Data on Data Mining (Healthcare insurance fraud detection using data mining): Information Technology – Data Mining

2024 MAY 10 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- New research on data mining is the subject of a new report. According to news reporting out of Bahria University by NewsRx editors, research stated, “Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods.”

The news reporters obtained a quote from the research from Bahria University: “Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. Methodolgy In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively.”

According to the news editors, the research concluded: “The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.”

For more information on this research see: Healthcare insurance fraud detection using data mining. BMC Medical Informatics and Decision Making, 2024,24(1):1-24. (BMC Medical Informatics and Decision Making - http://bmcmedinformdecismak.biomedcentral.com). The publisher for BMC Medical Informatics and Decision Making is BMC.

A free version of this journal article is available at https://doi.org/10.1186/s12911-024-02512-4.

Our news journalists report that more information may be obtained by contacting Zain Hamid, Department of Computer Science, Bahria University. Additional authors for this research include Fatima Khalique, Saba Mahmood, Ali Daud, Amal Bukhari, Bader Alshemaimri.

(Our reports deliver fact-based news of research and discoveries from around the world.)