Findings in Machine Learning Reported from City University of Hong Kong (Predictive Analysis for Healthcare Fraud Detection: Integration of Probabilistic Model and Interpretable Machine Learning): Machine Learning

2025 NOV 05 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Data detailed on Machine Learning have been presented. According to news reporting from Hong Kong, People’s Republic of China, by NewsRx journalists, research stated, “Medical insurance fraud detection is crucial for minimizing the depletion of insurance pools. While medical expense records (MER) are valuable for this task, their limited availability is often overlooked.”

Funders for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities, Stable support general projects-Stability Support Plan of Higher Education Institutions in Shenzhen.

The news correspondents obtained a quote from the research from the City University of Hong Kong, “Extant studies directly input MER into high-dimensional machine learning (ML) models to achieve fraud detection. Another class of models generates fraud prediction based on probability modelling of samples. To explore whether these two different classes of models can cross-fertilizer each other, this paper incorporates Bayesian network (BN) into extreme gradient boosting (XGB). After obtaining the healthcare fraud predictions, this study employs these results to risk management decisions for minimizing the cost of Medical Insurance Bureaus. To make the optimal cost-based decision, we develop an instance-dependent cost-sensitive XGB (ICXGB) method. Using real-world data, we construct various variables based on the famous Recency, Frequency and Monetary (RFM) principle and empirically assess the prediction performance of probabilistic model and ML. The integrative model shows a significant improvement over BN and ICXGB.”

According to the news reporters, the research concluded: “Finally, a post-hoc explanation method is adopted to quantify the contributions of the predictors and obtain some management implications.”

This research has been peer-reviewed.

For more information on this research see: Predictive Analysis for Healthcare Fraud Detection: Integration of Probabilistic Model and Interpretable Machine Learning. Information Sciences, 2025;719. Information Sciences can be contacted at: Elsevier Science Inc, Ste 800, 230 Park Ave, New York, NY 10169, USA. (Elsevier - www.elsevier.com; Information Sciences - http://www.journals.elsevier.com/information-sciences/)

Our news journalists report that additional information may be obtained by contacting Han-xiong Li, City University of Hong Kong, Dept. of Systems Engineering, Kowloon Tong, Hong Kong, People’s Republic of China. Additional authors for this research include Fei Xiao, Jian-qiang Wang, Xiao-kang Wang and Shui-xia Chen.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.ins.2025.122499. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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