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The labeling of instances by using the classification rules to the whole dataset. Thus, a transformed dataset is obtained by the model. In the development phase, the RF with SMOTE is applied against the training and testing data. Specifically, SMOTE adapted to balance data and sorts misclassified instances and finds the interesting instances. The results of the proposed model improvises the classifier performance RF with SMOTE when contrast with RF method.<\/p>","DOI":"10.4018\/ijec.2020100103","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T13:50:13Z","timestamp":1598449813000},"page":"30-47","source":"Crossref","is-referenced-by-count":3,"title":["Identifying Fraudulent Behaviors in Healthcare Claims Using Random Forest Classifier With SMOTEchnique"],"prefix":"10.4018","volume":"16","author":[{"family":"Naga Jyothi P.","sequence":"first","affiliation":[{"name":"Koneru Lakshmaiah Education Foundation, India"}]},{"family":"Rajya Lakshmi D.","sequence":"additional","affiliation":[{"name":"University College of Engineering Narasaraopeta, India"}]},{"family":"Rama Rao K. V. S. 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