Abstract
The advancement in healthcare services has been increasing widely to extend several services with intense quality. One of the important issues affecting the effective use of public funds is the detection of health insurance fraud. Previous techniques of detecting fraud pay close attention to characteristics of a single visit rather than many patient visits. Due to a higher false positive rate and poor profile construction, the common traits have reduced detection performance. This paper introduces a novel and intelligent Provider Fraud_Anomaly Detection System (PF_ADS) by combining big data and deep learning approaches for the healthcare insurance industry. The proposed framework contributes to improvising the preprocessing and classification phases to detect provider fraud at an untimely phase. Initially, the collected datasets are preprocessed using a Relative Risk-based MapReduce framework that builds an organized set of relationships between diseases, patients, and claiming variables. The classification phase is improvised using a proposed Recurrent Neural Network (RNN). It consists of sophisticated steps to consider the significant attributes using hyperparameter optimization. Recalling ability is one of the best parts of RNNs that defines the past and present states of the networks. Therefore, the ability of network state predictions and the tuning of parameters is studied by improved Decisional Score-based Bayesian Optimization (DS_BO). Finally, the best attributes with the selective hyperparameters are fed into the input layer of the Recurrent Neural Networks (RNNs) to classify the anomalies from the provider’s end. The proposed PF_ADS framework is experimented with and validated on the public repositories. The experimental results state that the proposed framework outperforms better than the other methods in terms of accuracy (88.09%), precision (14.15%), recall (32.80%), and 92.30 s computational time.
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Mary, A.J., Claret, S.P.A. Design and development of big data-based model for detecting fraud in healthcare insurance industry. Soft Comput 27, 8357–8369 (2023). https://doi.org/10.1007/s00500-023-08296-5
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DOI: https://doi.org/10.1007/s00500-023-08296-5