Medical Fraud and Abuse Detection System Based on Machine Learning

Zhang, Conghai; Xiao, Xinyao; Wu, Chao

doi:10.3390/ijerph17197265

Cited by 25 publications

(9 citation statements)

References 12 publications

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“…In [14], the fraud detection model is proposed and disease-drug based relationship is used to identify outliers. The k-means and isolation forest algorithms are used on different datasets and it is identified that isolation forests perform better than k-means clustering for the model.…”

Section: Background and Related Workmentioning

confidence: 99%

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

et al. 2022

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With the exponential rise in government and private health-supported schemes, the number of fraudulent billing cases is also increasing. Detection of fraudulent transactions in healthcare systems is an exigent task due to intricate relationships among dynamic elements, including doctors, patients, and services. Hence, to introduce transparency in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in existing procedures, so that the fraudulent medical billing cases can be accurately identified. Moreover, there is also a need to optimize both the cost burden for the service provider and medical benefits for the client. This paper presents a novel process-based fraud detection methodology to detect insurance claim-related frauds in the healthcare system using sequence mining concepts. Recent literature focuses on the amount-based analysis or medication versus disease sequential analysis rather than detecting frauds using sequence generation of services within each specialty. The proposed methodology generates frequent sequences with different pattern lengths. The confidence values and confidence level are computed for each sequence. The sequence rule engine generates frequent sequences along with confidence values for each hospital's specialty and compares them with the actual patient values. This identifies anomalies as both sequences would not be compliant with the rule engine's sequences. The process-based fraud detection methodology is validated using last five years of a local hospital's transactional data that includes many reported cases of fraudulent activities.

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Section: Background and Related Workmentioning

confidence: 99%

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

et al. 2022

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“…An anomaly detection technique was used to measure the likelihood of fraudulent health insurance claims records based on historical claims (Kirlidog & Asuk, 2012). Another study (Zhang et al, 2020) detected anomalies by quantifying disease-drug relationships in association scores and other features with a neural network with fully connected layers and infrequent convolutions.The study introduced focal-loss function to adapt to unbalanced data as well as relative probability scores to measure model performance. In another study (Herland et al, 2019), random under sampling (RUS) and SMOTE were applied to deal with imbalance data.…”

Section: Related Workmentioning

confidence: 99%

Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim

Nugraha¹,

Pardede²,

Subekti³

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Fraud on health insurance impacts cost overruns and a quality decline in health services in the long term. The use of machine learning to detect fraud on health insurance is increasingly popular. However, one challenge in predicting health insurance fraud is the data imbalance. The data imbalance can cause a bias towards the majority class in many machine learning methods. Oversampling is a solution for data imbalance by augmenting new data based on the existing minority class data. Recently, there has been growing interest in employing deep learning for data augmentation. One of them is using Generative Adversarial Networks (GAN). This paper proposes using GAN as an oversampling method to generate additional data for minority classes. Since data for detecting health insurance fraud are tabular, we adopt Conditional Tabular GAN (CTGAN) architecture where the generator is conditioned to adjust the tabular data input and receive additional information to produce samples according to the specified class conditions. The new balanced data are used to train 17 classification algorithms. Our experiments showed that the proposed method performs better than other oversampling methods on several evaluation metrics, i.e., accuracy, precision score, F1-score, and ROC.

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“…In Reference [47] the authors deploy a one‐class classification algorithm to identify the products' quality abnormalities through the analysis of manufacturing, inspection and after‐sales service data. While in Reference [48], medical frauds and abuses are detected using different clustering‐based algorithms, that is, DBSCAN, LOF, Isolation Forest, and K‐means. Because of the sensitivity of medical data, anomaly detection plays a major role in preserving the privacy of the patients.…”

Section: Related Workmentioning

confidence: 99%

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

et al. 2021

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Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end‐users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one‐class support vector machine, namely UAD‐OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD‐M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule‐based algorithm is introduced to extract the micromoments representing the intent‐rich moments, in which the end‐users make decisions to consume energy; and (iii) an improved K‐nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD‐M2 achieves both a highest abnormality detection performance and real‐time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real‐world data set collected at the Qatar University energy lab.

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Medical Fraud and Abuse Detection System Based on Machine Learning

Cited by 25 publications

References 12 publications

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

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