Additionally, exploring the integration of association rule mining with other techniques like unsupervised classifiers or ensemble methods could further enhance the accuracy and effectiveness of fraud detection systems in the healthcare insurance domain. Materials & methods Dataset In this study, the ...
In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed...
To analyze the data for fraud detection, a selection of prescription data from the year 2018 were examined. The Local Correlation Integral algorithm was applied to detect any outliers on the dataset. The results revealed that 7 out of 879 cases could be characterized as outliers. These outliers...
and different healthIoTdevices. These data require proper management and analysis to produce meaningful information. Managing and analyzing this vast dataset is very time consuming and expensive with conventional methods. Therefore, providing relevant solutions for improvinghealthcare services; industries are...
Our Process Aggregate Information is automatically collected from more than 20,000 sources of public records, providing a comprehensive 15-year history for each provider. Codify Each dataset is normalized and codified, bringing standards to align across many different sources. ...
There are a number of limitations and future directions to our study. Given that Chinese commercial health insurance companies are still in the process of exploring the use of health big data, and have not yet formed a complete healthcare big data dataset, it is impossible to quantitatively mea...
track patients' medical information and construct additional units. Numerous tests are run to assess the proposed Health Chain model's performance, and the findings demonstrate that the proposed suggestion effectively manages a huge dataset called (nCOV19) from the Covid-19 study with little delay....
In healthcare, addressing the timing factor in KG creation is critical. Ma et al. [144] developed a temporal KG that is useful for studying episodic memory in cognitive tasks. The Integrated Conflict Early Warning System (ICEWS) dataset and the Global Database of Events, Language, and Tone ...
In healthcare, addressing the timing factor in KG creation is critical. Ma et al. [144] developed a temporal KG that is useful for studying episodic memory in cognitive tasks. The Integrated Conflict Early Warning System (ICEWS) dataset and the Global Database of Events, Language, and Tone ...
The EEG signals are classified for seizure detection. The 1-D DNN model is proposed with improved cross-entropy. The proposed model consists of one input layer, eight hidden layers and one output layer. Initially, the DNN model is trained with the University of Bonn (UoB) dataset [34] that...