KDE-OCSVM model using Kullback-Leibler divergence to detect anomalies in medical claims
作者：
Highlights：
• An anomaly detection method is developed for the records of UEBMI.
• A combination method is utilized to preprocess the experimental data.
• A feature selection method involves two aspects, variance and similarity.
• An extended OCSVM model is established to improve the model performance.
• Interesting findings provide some guideline for the future research and practical issues.
摘要
•An anomaly detection method is developed for the records of UEBMI.•A combination method is utilized to preprocess the experimental data.•A feature selection method involves two aspects, variance and similarity.•An extended OCSVM model is established to improve the model performance.•Interesting findings provide some guideline for the future research and practical issues.
论文关键词：Anomaly detection,Medical insurance,Kernel density estimation,Kullback-Leibler divergence,Support vector machine
论文评审过程：Received 20 October 2021, Revised 22 January 2022, Accepted 28 March 2022, Available online 4 April 2022, Version of Record 6 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117056
