Comparisons of SVM Kernels for Insurance Data Clustering

Irfan Nurhidayat, Busayamas Pimpunchat, Samad Noeiaghdam, Unai Fernández-Gámiz

Abstract


This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data.

 

Doi: 10.28991/ESJ-2022-06-04-014

Full Text: PDF


Keywords


Insurance Data Clustering; Support Vector Machine; RMSE; AUC; Sum Insured.

References


Tangcharoensathien, V., Tisayaticom, K., Suphanchaimat, R., Vongmongkol, V., Viriyathorn, S., & Limwattananon, S. (2020). Financial risk protection of Thailand’s universal health coverage: Results from series of national household surveys between 1996 and 2015. International Journal for Equity in Health, 19(1), 1–12. doi:10.1186/s12939-020-01273-6.

Paek, S. C., Meemon, N., & Wan, T. T. H. (2016). Thailand’s universal coverage scheme and its impact on health-seeking behavior. SpringerPlus, 5(1), 1–16. doi:10.1186/s40064-016-3665-4.

Suraratdecha, C., Saithanu, S., & Tangcharoensathien, V. (2005). Is universal coverage a solution for disparities in health care? Findings from three low-income provinces of Thailand. Health Policy, 73(3), 272–284. doi:10.1016/j.healthpol.2004.11.019.

Carrin, G., Waelkens, M. P., & Criel, B. (2005). Community-based health insurance in developing countries: A study of its contribution to the performance of health financing systems. Tropical Medicine and International Health, 10(8), 799–811. doi:10.1111/j.1365-3156.2005.01455.x.

Pauly, M. V., Zweifel, P., Scheffler, R. M., Preker, A. S., & Bassett, M. (2006). Private health insurance in developing countries. Health Affairs, 25(2), 369–379. doi:10.1377/hlthaff.25.2.369.

Zanaty, E. A., & Afifi, A. (2011). Support vector machines (SVMs) with universal kernels. Applied Artificial Intelligence, 25(7), 575–589. doi:10.1080/08839514.2011.595280.

Buchanna, G., Premchand, P., & Govardhan, A. (2022). Classification of Epileptic and Non-Epileptic Electroencephalogram (EEG) Signals Using Fractal Analysis and Support Vector Regression. Emerging Science Journal, 6(1), 138-150. doi:10.28991/ESJ-2022-06-01-011.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning, 112, 1-18. Springer, New York, United States. doi:10.1007/978-1-0716-1418-1.

Shah, T. I., Milosavljevic, S., & Bath, B. (2017). Measuring geographical accessibility to rural and remote health care services: Challenges and considerations. Spatial and Spatio-Temporal Epidemiology, 21, 87–96. doi:10.1016/j.sste.2017.04.002.

Sritart, H., Tuntiwong, K., Miyazaki, H., & Taertulakarn, S. (2021). Disparities in healthcare services and spatial assessments of mobile health clinics in the border regions of Thailand. International Journal of Environmental Research and Public Health, 18(20), 1–24. doi:10.3390/ijerph182010782.

Rojjananukulpong, R., Ahmad, M. M., & Saqib, S. E. (2021). Disparities in physical accessibility among rural Thais under universal health coverage. American Journal of Tropical Medicine and Hygiene, 105(3), 837–845. doi:10.4269/ajtmh.20-1627.

Marshall, A. I., Kantamaturapoj, K., Kiewnin, K., Chotchoungchatchai, S., Patcharanarumol, W., & Tangcharoensathien, V. (2021). Participatory and responsive governance in universal health coverage: An analysis of legislative provisions in Thailand. BMJ Global Health, 6(2), 1–10. doi:10.1136/bmjgh-2020-004117.

Sumriddetchkajorn, K., Shimazaki, K., Ono, T., Kusaba, T., Sato, K., & Kobayashi, N. (2019). Universal health coverage and primary care, Thailand. Bulletin of the World Health Organization, 97(6), 415–422. doi:10.2471/BLT.18.223693.

Yiengprugsawan, V., Carmichael, G. A., Lim, L. L. Y., Seubsman, S., & Sleigh, A. C. (2010). Has universal health insurance reduced socioeconomic inequalities in urban and rural health service use in Thailand? Health and Place, 16(5), 1030–1037. doi:10.1016/j.healthplace.2010.06.010.

Yiengprugsawan, V., Seubsman, S. ang, & Sleigh, A. C. (2012). Health, Well-being, and Social Indicators among Monks, Prisoners, and Other Adult Members of an Open University Cohort in Thailand. Journal of Religion and Health, 51(3), 925–933. doi:10.1007/s10943-010-9410-3.

Yiengprugsawan, V., Lim, L. L. Y., Carmichael, G. A., Seubsman, S. A., & Sleigh, A. C. (2009). Tracking and Decomposing Health and Disease Inequality in Thailand. Annals of Epidemiology, 19(11), 800–807. doi:10.1016/j.annepidem.2009.04.009.

Puenpatom, R. A., & Rosenman, R. (2008). Efficiency of Thai provincial public hospitals during the introduction of universal health coverage using capitation. Health Care Management Science, 11(4), 319–338. doi:10.1007/s10729-008-9057-8.

Chandanachulaka, S. (2020). Thailand: Country report on children’s environmental health. Reviews on Environmental Health, 35(1), 71–77. doi:10.1515/reveh-2019-0082.

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., & Müller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12(1), 1–8. doi:10.1186/1471-2105-12-77.

SIB Swiss Institute of Bioinformatics (2022). PROC: Display and analyze ROC curves in R and S+. Available online: https://web.expasy.org/pROC/ (accessed on March 2022).

Campbell, J.J. (2022). SVM with CARET: Loading required R packages. Available online: https://rpubs.com/uky994/593668 (accessed on February 2022).


Full Text: PDF

DOI: 10.28991/ESJ-2022-06-04-014

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Busayamas Pimpunchat, Irfan Nurhidayat, Samad Noeiaghdam, Unai Fernandez-Gamiz