Classification and identification of medical insurance fraud: a case-based reasoning approach
DOI: https://doi.org/10.3846/tede.2025.23597Abstract
Appropriate classification of medical insurance fraud events can not only be effective in preventing and combating fraud, but also greatly improve the utilization of medical resources. Due to the uncertainty inherent in medical insurance fraud, identifying and classifying the fraud are non-trivial tasks. In addition, the selection of classification radius by traditional methods is often highly subjective. To this end, a case-based reasoning (CBR) approach in probabilistic hesitant fuzzy environment and its application to classifying the severity of medical insurance fraud events are investigated in this article. At first, the probabilistic hesitant fuzzy element (PHFE) is regarded as a discrete probability distribution, and its distribution function is defined. On this basis, a distribution discrepancy degree is proposed to make up for the shortage of existing measures between PHFEs. Then, a probabilistic hesitant fuzzy decision-making method based on CBR is proposed, which considers both decision data and the expert’s own knowledge and experience. Finally, the proposed method is used to classify the severity of medical insurance fraud events, and the rationality and superiority of the method are verified by comparative analysis.
First published online 15 July 2025
Keywords:
medical insurance fraud, probabilistic hesitant fuzzy element, distribution function, distribution discrepancy degree, case-based reasoningHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications, 7(1), 39–59. https://doi.org/10.3233/AIC-1994-7104
Ataabadi, P. E., Neysiani, B. S., Nogorani, M. Z., & Mehraby, N., (2022, May 11–12). Semi-supervised medical insurance fraud detection by predicting indirect reductions rate using machine learning generalization capability. In Proceedings of the 2022 8th International Conference on Web Research (ICWR) (pp. 176–182). Tehran, Iran, Islamic Republic of. IEEE. https://doi.org/10.1109/ICWR54782.2022.9786251
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Cao, Q., Liu, X. D., Wang, Z. W., Zhang, S. T., & Wu, J. (2020). Recommendation decision-making algorithm for sharing accommodation using probabilistic hesitant fuzzy sets and bipartite network projection. Complex & Intelligent Systems, 6, 431–445. https://doi.org/10.1007/s40747-020-00142-7
Chen, H. Y., Wu, Z. Y., Chen, T. L., Huang, Y. M., & Liu, C. H. (2021). Security privacy and policy for cryptographic based electronic medical information system. Sensors, 21(3), Article 713. https://doi.org/10.3390/s21030713
Divsalar, M., Ahmadi, M., Ebrahimi, E., & Ishizaka, A. (2022). A probabilistic hesitant fuzzy Choquet integral-based TODIM method for multi-attribute group decision-making. Expert Systems with Applications, 191, Article 116266. https://doi.org/10.1016/j.eswa.2021.116266
Fan, Z. P., Li, Y. H., Wang, X., & Liu, Y. (2014). Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosion. Expert Systems with Applications, 41(5), 2526–2534. https://doi.org/10.1016/j.eswa.2013.09.051
Gorzałczany, M. B. (1987). A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets and Systems, 21(1), 1–17. https://doi.org/10.1016/0165-0114(87)90148-5
Guida, S. (2021). Ransomware attacks are data breaches and finally started to be reported properly by major companies. European Journal of Privacy Law & Technologies, 7, 1–3. https://universitypress.unisob.na.it/ojs/index.php/ejplt/article/view/1374
Han, X. R., & Zhan, J. M. (2023). A sequential three-way decision-based group consensus method under probabilistic linguistic term sets. Information Sciences, 624, 567–589. https://doi.org/10.1016/j.ins.2022.12.111
Han, X. R., Zhang, C., & Zhan, J. M. (2022). A three-way decision method under probabilistic linguistic term sets and its application to Air Quality Index. Information Sciences, 617, 254–276. https://doi.org/10.1016/j.ins.2022.10.108
Jiang, J. C., Liu, X. D., Garg, H., & Zhang, S. T. (2023). Large group decision-making based on interval rough integrated cloud model. Advanced Engineering Informatics, 56, Article 101964. https://doi.org/10.1016/j.aei.2023.101964
Jiang, J. C., Liu, X. D., Wang, Z. W., Ding, W. P., & Zhang, S. T. (2024). Large group emergency decision-making with bi-directional trust in social networks: A probabilistic hesitant fuzzy integrated cloud approach. Information Fusion, 102, Article 102062. https://doi.org/10.1016/j.inffus.2023.102062
Jiang, X. S., Lin, K. B., Zeng, Y. F., & Yang, F., (2021, August, 17–21). Medical insurance medication anomaly detection based on isolated forest proximity matrix. In Proceedings of the 2021 16th International Conference on Computer Science & Education (ICCSE) (pp. 512–517). Lancaster, United Kingdom. IEEE. https://doi.org/10.1109/ICCSE51940.2021.9569723
Kapadiya, K., Patel, U., Gupta, R., Alshehri, M. D., Tanwar, S., Sharma, G., & Bokoro, P. N. (2022). Blockchain and AI-empowered healthcare insurance fraud detection: An analysis, architecture, and future prospects. IEEE Access, 10, 79606–79627. https://doi.org/10.1109/ACCESS.2022.3194569
Krishankumar, R., Ravichandran, K. S., Liu, P. D., Kar, S., & Gandomi, A. H. (2021). A decision framework under probabilistic hesitant fuzzy environment with probability estimation for multi-criteria decision making. Neural Computing and Applications, 33(14), 8417–8433. https://doi.org/10.1007/s00521-020-05595-y
Li, J., Lan, Q. L., Zhu, E. Y., Xu, Y., & Zhu, D. (2022). A study of health insurance fraud in China and recommendations for fraud detection and prevention. Journal of Organizational and End User Computing (JOEUC), 34(4), 1–19. https://doi.org/10.4018/JOEUC.301271
Li, P., Liu, J., Yang, Y. J., & Wei, C. P. (2020). Evaluation of poverty-stricken families in rural areas using a novel case-based reasoning method for probabilistic linguistic term sets. Computers & Industrial Engineering, 147, Article 106658. https://doi.org/10.1016/j.cie.2020.106658
Li, P., & Wei, C. P. (2018). A case-based reasoning decision-making model for hesitant fuzzy linguistic information. International Journal of Fuzzy Systems, 20, 2175–2186. https://doi.org/10.1007/s40815-017-0391-1
Liao, T. W., Zhang, Z., & Mount, C. R. (1998). Similarity measures for retrieval in case-based reasoning systems. Applied Artificial Intelligence, 12(4), 267–288. https://doi.org/10.1080/088395198117730
Liao, N. N., Wei, G. W., & Chen, X. D. (2022). TODIM method based on cumulative prospect theory for multiple attributes group decision making under probabilistic hesitant fuzzy setting. International Journal of Fuzzy Systems, 24(1), 322–339. https://doi.org/10.1007/s40815-021-01138-2
Liu, X. D., Wang, Z. W., Zhang, S. T., & Garg, H. (2021). Novel correlation coefficient between hesitant fuzzy sets with application to medical diagnosis. Expert Systems with Applications, 183, Article 115393. https://doi.org/10.1016/j.eswa.2021.115393
Liu, X. D., Wu, J., Zhang, S. T., Wang, Z. W., & Garg, H. (2022). Extended cumulative residual entropy for emergency group decision-making under probabilistic hesitant fuzzy environment. International Journal of Fuzzy Systems, 24(1), 159–179. https://doi.org/10.1007/s40815-021-01122-w
Liu, S. J., & Guo, Z. X. (2022). Probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measurement. Journal of Intelligent & Fuzzy Systems, 43(5), 5953–5964. https://doi.org/10.3233/JIFS-213427
Loève, M. (2017). Probability theory. Courier Dover Publications.
Löw, N., Hesser, J., & Blessing, M. (2019). Multiple retrieval case-based reasoning for incomplete datasets. Journal of Biomedical Informatics, 92, Article 103127. https://doi.org/10.1016/j.jbi.2019.103127
Naeem, M., Khan, M. A., Abdullah, S., Qiyas, M., & Khan, S. (2021). Extended TOPSIS method based on the entropy measure and probabilistic hesitant fuzzy information and their application in decision support system. Journal of Intelligent & Fuzzy Systems, 40(6), 11479–11490. https://doi.org/10.3233/JIFS-202700
Schank, R. C. (1983). Dynamic memory: A theory of reminding and learning in computers and people. Cambridge University Press.
Settipalli, L., & Gangadharan, G. R. (2023). Provider profiling and labeling of fraudulent health insurance claims using Weighted MultiTree. Journal of Ambient Intelligence and Humanized Computing, 14, 3487–3508. https://doi.org/10.1007/s12652-021-03481-6
Sha, X. Y., Yin, C. C., Xu, Z. S., & Zhang, S. (2021). Probabilistic hesitant fuzzy TOPSIS emergency decision-making method based on the cumulative prospect theory. Journal of Intelligent & Fuzzy Systems, 40(3), 4367–4383. https://doi.org/10.3233/JIFS-201119
Song, C. Y., Xu, Z. S., & Zhao, H. (2019). New correlation coefficients between probabilistic hesitant fuzzy sets and their applications in cluster analysis. International Journal of Fuzzy Systems, 21(2), 355–368. https://doi.org/10.1007/s40815-018-0578-0
Song, H. F., & Chen, Z. C. (2021). Multi-attribute decision-making method based distance and COPRAS method with probabilistic hesitant fuzzy environment. International Journal of Computational Intelligence Systems, 14(1), 1229–1241. https://doi.org/10.2991/ijcis.d.210318.001
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539. https://doi.org/10.1002/int.20418
Villegas-Ortega, J., Bellido-Boza, L., & Mauricio, D. (2021). Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: A scoping review. Health & Justice, 9, 1–23. https://doi.org/10.1186/s40352-021-00149-3
Wang, D. L., Wan, K. D., & Ma, W. X. (2020). Emergency decision-making model of environmental emergencies based on case-based reasoning method. Journal of Environmental Management, 262, Article 110382. https://doi.org/10.1016/j.jenvman.2020.110382
Wang, Y. B., Jia, X. L., & Zhang, L. X. (2022). Evaluation of the survival of Yangtze finless porpoise under probabilistic hesitant fuzzy environment. International Journal of Intelligent Systems, 37(10), 7665–7684. https://doi.org/10.1002/int.22898
Wang, Z. X., & Li, J. (2017). Correlation coefficients of probabilistic hesitant fuzzy elements and their applications to evaluation of the alternatives. Symmetry, 9(11), Article 259. https://doi.org/10.3390/sym9110259
Wu, W. Y., Ni, Z. W., Jin, F. F., Wu, J., Li, Y., & Li, P. (2021). Investment selection based on Bonferroni mean under generalized probabilistic hesitant fuzzy environments. Mathematics, 9(1), Article 107. https://doi.org/10.3390/math9010107
Xu, T. T., Zhang, H., & Li, B. Q. (2022). Fuzzy entropy and hesitancy entropy in probabilistic hesitant fuzzy information and their applications. Soft Computing, 26(18), 9101–9115. https://doi.org/10.1007/s00500-022-07309-z
Xu, X. H., Huang, Y. X., & Chen, K. (2019). Method for large group emergency decision making with complex preferences based on emergency similarity and interval consistency. Natural Hazards, 97, 45–64. https://doi.org/10.1007/s11069-019-03624-1
Xu, Z. S., & Zhou, W. (2017). Consensus building with a group of decision makers under the hesitant probabilistic fuzzy environment. Fuzzy Optimization and Decision Making, 16, 481–503. https://doi.org/10.1007/s10700-016-9257-5
Yu, F., Li, X. Y., & Han, X. S. (2018). Risk response for urban water supply network using case-based reasoning during a natural disaster. Safety Science, 106, 121–139. https://doi.org/10.1016/j.ssci.2018.03.003
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zhan, J. M., Wang, J. J., Ding, W. P., & Yao, Y. Y. (2022). Three-way behavioral decision making with hesitant fuzzy information systems: Survey and challenges. IEEE/CAA Journal of Automatica Sinica, 10(2), 330–350. https://doi.org/10.1109/JAS.2022.106061
Zhang, G. M., Zhang, X. Y., Bilal, M., Dou, W. C., Xu, X. L., & Rodrigues, J. J. (2022). Identifying fraud in medical insurance based on blockchain and deep learning. Future Generation Computer Systems, 130, 140–154. https://doi.org/10.1016/j.future.2021.12.006
Zhang, J. J. (2021). Behavior types and legal governance of medical insurance fraud. Social Sciences Review, 36(4), 123–129. https://doi.org/10.16745/j.cnki.cn62-1110/c.2021.04.021
Zhang, S. S., Liu, X. D., Garg, H., & Zhang, S. T. (2023). Investment decision making in the fuzzy context: An integrated model approach. Journal of Intelligent & Fuzzy Systems, 44(3), 3763–3786. https://doi.org/10.3233/JIFS-223059
Zhang, W. M., Liu, X. Y., Zhang, X. Y., Hu, W. H., Zhang, J. C., & Shao, W. Y., (2022, May 6–8). Medicare fraud gang discovery based on community discovery algorithms. In Proceedings of the 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) (pp. 206–211). Jinan, China. IEEE. https://doi.org/10.1109/BigDataSecurityHPSCIDS54978.2022.00047
Zheng, J., Wang, Y. M., & Chen, S. Q. (2016). Dynamic case retrieval method with subjective preferences and objective information for emergency decision making. IEEE/CAA Journal of Automatica Sinica, 5(3), 749–757. https://doi.org/10.1109/JAS.2016.7510232
Zhou, X. Y., & Su, W. H. (2021). Exploring the audit supervision of China’s medical insurance fund based on the 2019 medical insurance fund audit data. Accounting & Finance Research, 10, 78–86. https://doi.org/10.5430/AFR.V10N2P78
Zhu, B. (2014). Decision method for research and application based on preference relation. Southeast University, Nanjing, China.
Zhu, J. X., Ma, X. L., Kou, G., Herrera-Viedma, E., & Zhan, J. M. (2023a). A three-way consensus model with regret theory under the framework of probabilistic linguistic term sets. Information Fusion, 95, 250–274. https://doi.org/10.1016/j.inffus.2023.02.029
Zhu, J. X., Ma, X. L., Martínez, L., & Zhan, J. M. (2023b). A probabilistic linguistic three-way decision method with regret theory via fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 31(8), 2821–2835. https://doi.org/10.1109/TFUZZ.2023.3236386
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.