CLASSIFICATION OF THE FINANCIAL SUSTAINABILITY OF HEALTH INSURANCE BENEFICIARIES THROUGH DATA MINING TECHNIQUES

Sílvia Rebouças (), Daniele Oliveira (), Rômulo Soares (), Eugénia Ferreira () and Maria José Gouveia ()
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Sílvia Rebouças: Federal University of Ceará, Postal: Adjunct Professor at, Faculty of Economics, Management, Actuary and Accounting, Federal University of Ceará, Brazil, http://www.ufc.br/
Daniele Oliveira: Metropolitan Faculty of Grande Fortaleza, Postal: Professor at, Metropolitan Faculty of Grande Fortaleza, Brazil, http://faculdadesja.com.br/faculdades/faculdade-metropolitana-da-grande-fortaleza-fametro
Rômulo Soares: Federal University of Ceará, Postal: Professor of Statistics at, Faculty of Economics, Management, Actuary and Accounting, Federal University of Ceará, Brazil, http://www.ufc.br/
Eugénia Ferreira: CIEO - Research Centre for Spatial and Organizational Dynamics, Postal: Assistant Professor at, Faculty of Economics/CIEO, University of Algarve, Campus de Gambelas, Faro, Portugal, http://www.cieo.pt/
Maria José Gouveia: Health School of the University of Algarve, Postal: Coordinator Professor at, Health School, University of Algarve, Avenida Dr. Adelino da Palma Carlos, Faro, Portugal, http://ess.ualg.pt/pt

Journal of Tourism, Sustainability and Well-being, 2016, vol. 4, issue 3, 229-242

Abstract: Advances in information technologies have led to the storage of large amounts of data by organizations. An analysis of this data through data mining techniques is important support for decision-making. This article aims to apply techniques for the classification of the beneficiaries of an operator of health insurance in Brazil, according to their financial sustainability, via their sociodemographic characteristics and their healthcare cost history. Beneficiaries with a loss ratio greater than 0.75 are considered unsustainable. The sample consists of 38875 beneficiaries, active between the years 2011 and 2013. The techniques used were logistic regression and classification trees. The performance of the models was compared to accuracy rates and receiver operating Characteristic curves (ROC curves), by determining the area under the curves (AUC). The results showed that most of the sample is composed of sustainable beneficiaries. The logistic regression model had a 68.43% accuracy rate with AUC of 0.7501, and the classification tree obtained 67.76% accuracy and an AUC of 0.6855. Age and the type of plan were the most important variables related to the profile of the beneficiaries in the classification. The highlights with regard to healthcare costs were annual spending on consultation and on dental insurance.

Keywords: Data Mining; Logistic Regression; Classification Trees; Health Insurance (search for similar items in EconPapers)
JEL-codes: C55 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:ris:jspord:0919

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