Classification of the financial sustainability of health insurance beneficiaries through data mining techniques
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 o...
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| Format: | Article |
| Language: | English |
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Research Centre for Tourism, Sustainability and Well-being - CinTurs; University of Algarve
2016-09-01
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| Series: | Journal of Spatial and Organizational Dynamics |
| Subjects: | |
| Online Access: | http://www.cieo.pt/journal/J_3_2016/article4.pdf |
| _version_ | 1818038494357880832 |
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| author | Sílvia Maria Dias Pedro Rebouças Daniele Adelaide Brandão de Oliveira Rômulo Alves Soares Eugénia Maria Dores Maia Ferreira Maria José Baltazar dos Reis de Pinto Gouveia |
| author_facet | Sílvia Maria Dias Pedro Rebouças Daniele Adelaide Brandão de Oliveira Rômulo Alves Soares Eugénia Maria Dores Maia Ferreira Maria José Baltazar dos Reis de Pinto Gouveia |
| author_sort | Sílvia Maria Dias Pedro Rebouças |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T07:43:38Z |
| format | Article |
| id | doaj.art-a06546763f30435580025f5ad53bb3e7 |
| institution | Directory Open Access Journal |
| issn | 2183-1912 2183-1912 |
| language | English |
| last_indexed | 2024-12-10T07:43:38Z |
| publishDate | 2016-09-01 |
| publisher | Research Centre for Tourism, Sustainability and Well-being - CinTurs; University of Algarve |
| record_format | Article |
| series | Journal of Spatial and Organizational Dynamics |
| spelling | doaj.art-a06546763f30435580025f5ad53bb3e72022-12-22T01:57:14ZengResearch Centre for Tourism, Sustainability and Well-being - CinTurs; University of AlgarveJournal of Spatial and Organizational Dynamics2183-19122183-19122016-09-01IV3229242Classification of the financial sustainability of health insurance beneficiaries through data mining techniquesSílvia Maria Dias Pedro Rebouças0Daniele Adelaide Brandão de Oliveira1Rômulo Alves Soares2Eugénia Maria Dores Maia Ferreira3Maria José Baltazar dos Reis de Pinto Gouveia4Federal University of CearáFanor Devry BrazilFederal University of Ceará University of Algarve, Faculty of Economics, Research Centre for Spatial and Organizational Dynamics University of AlgarveAdvances 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.http://www.cieo.pt/journal/J_3_2016/article4.pdfData MiningLogistic RegressionClassification TreesHealth Insurance |
| spellingShingle | Sílvia Maria Dias Pedro Rebouças Daniele Adelaide Brandão de Oliveira Rômulo Alves Soares Eugénia Maria Dores Maia Ferreira Maria José Baltazar dos Reis de Pinto Gouveia Classification of the financial sustainability of health insurance beneficiaries through data mining techniques Journal of Spatial and Organizational Dynamics Data Mining Logistic Regression Classification Trees Health Insurance |
| title | Classification of the financial sustainability of health insurance beneficiaries through data mining techniques |
| title_full | Classification of the financial sustainability of health insurance beneficiaries through data mining techniques |
| title_fullStr | Classification of the financial sustainability of health insurance beneficiaries through data mining techniques |
| title_full_unstemmed | Classification of the financial sustainability of health insurance beneficiaries through data mining techniques |
| title_short | Classification of the financial sustainability of health insurance beneficiaries through data mining techniques |
| title_sort | classification of the financial sustainability of health insurance beneficiaries through data mining techniques |
| topic | Data Mining Logistic Regression Classification Trees Health Insurance |
| url | http://www.cieo.pt/journal/J_3_2016/article4.pdf |
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