New Machine Learning Study Findings Has Been Reported by a Researcher at Health Informatics Department (Fraud Detection in Healthcare Insurance Claims Using Machine Learning): Machine Learning

2023 SEP 21 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Current study results on artificial intelligence have been published. According to news reporting from Riyadh, Saudi Arabia, by NewsRx journalists, research stated, “Healthcare fraud is intentionally submitting false claims or producing misinterpretation of facts to obtain entitlement payments. Thus, it wastes healthcare financial resources and increases healthcare costs.”

Our news editors obtained a quote from the research from Health Informatics Department: “Subsequently, fraud poses a substantial financial challenge. Therefore, supervised machine and deep learning analytics such as random forest, logistic regression, and artificial neural networks are successfully used to detect healthcare insurance fraud. This study aims to develop a health model that automatically detects fraud from health insurance claims in Saudi Arabia. The model indicates the greatest contributing factor to fraud with optimal accuracy. The labeled imbalanced dataset used three supervised deep and machine learning methods. The dataset was obtained from three healthcare providers in Saudi Arabia. The applied models were random forest, logistic regression, and artificial neural networks. The SMOT technique was used to balance the dataset. Boruta object feature selection was applied to exclude insignificant features. Validation metrics were accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC). Random forest classifiers indicated policy type, education, and age as the most significant features with an accuracy of 98.21%, 98.08% precision, 100% recall, an F1 score of 99.03%, specificity of 80%, and an AUC of 90.00%.”

According to the news editors, the research concluded: “Logistic regression resulted in an accuracy of 80.36%, 97.62% precision, 80.39% recall, an F1 score of 88.17%, specificity of 80%, and an AUC of 80.20%. ANN revealed an accuracy of 94.64%, 98.00% precision, 96.08% recall, an F1 score of 97.03%, a specificity of 80%, and an AUC of 88.04%. This predictive analytics study applied three successful models, each of which yielded acceptable accuracy and validation metrics; however, further research on a larger dataset is advised.”

For more information on this research see: Fraud Detection in Healthcare Insurance Claims Using Machine Learning. Risks, 2023,11(9). (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks11090160.

Our news journalists report that additional information may be obtained by contacting Eman Nabrawi, Health Informatics Division, King Saud Ibn Abdulaziz University for Health Sciences, P.O. Box 3660, Riyadh 11481, Saudi Arabia. Additional authors for this research include Abdullah Alanazi.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the author of this research: Abdullah Alanazi (http://orcid.org/0000-0001-8400-0742).

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