IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT

Authors

  • Mrs. K. Aarati
  • A. Shirisha
  • K. Bindhu

DOI:

https://doi.org/10.46243/jst.2023.v8.i07.pp45-57

Keywords:

Insurance, Healthcare, Fraud Detection, SVM, Decision Tree, Logistic Regression, KNN

Abstract

— Patients depend on health insurance provided by the governmentsystems, private systems, or both to utilizethe high-priced healthcare expenses. Thisdependency on health insurance draws some healthcare service providers to commit insurance frauds. In this paper, we perform a comparative analysis on various classification algorithms, namely Support Vector Machine (SVM), Decision-Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), to detect the health insurance fraud. The effectiveness of the algorithms are observed on the basis of performance metrics: Precision, Recall and F1-Score

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Published

2023-07-18

How to Cite

Mrs. K. Aarati, A. Shirisha, & K. Bindhu. (2023). IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT. Journal of Science & Technology (JST), 8(7), 45–57. https://doi.org/10.46243/jst.2023.v8.i07.pp45-57